{"after":"WzE3OTAxMjIzMTUyNDMsMTEuMDUxMDkxLDEsImM3NGY1OTQ3LWI2OTQtNDEzMS04MDdiLTM3YmQxYjEyOWYxMCJd","results":[{"_score":14.239145,"_sort":[1790122770073,14.239145,1,"2bbdd079-e042-41f2-8f5d-71e5a055e550"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GPM Ground Validation (GV) Environment Canada (EC) Visibility Sensor FD12P C3VP dataset consists of visibility and precipitation data collected at the Environment Canada Canadian Climate station at the Centre for Atmospheric Research Experiments (CARE) site during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The GPM GV Visibility Sensor FD12P C3VP data are available from October 4, 2006 through March 31, 2007 in a Microsoft Excel comma-separated variable spreadsheet.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1995871445-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmvisecc3vp__1/docs/gpmvisecc3vp_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://radarmet.atmos.colostate.edu/c3vp/petersenetal_C3VP.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1995871445-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.manualslib.com/manual/538824/Vaisala-Fd12p.html","format":"HTML","mediaType":"text/html"}],"identifier":"10.5067/GPMGV/C3VP/VISSENSOR/DATA101","keyword":["earth-science-air-quality-atmosphere-visibility","earth-science-precipitation-atmosphere-liquid-precipitation"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -79.791, \"NorthBoundingCoordinate\": 44.242, \"EastBoundingCoordinate\": -79.772, \"SouthBoundingCoordinate\": 44.222}]]","temporal":"2006-10-04/2007-03-31","theme":["Earth Science"],"title":"GPM Ground Validation Environment Canada (EC) Visibility Sensor FD12P C3VP V1"},"description":"The GPM Ground Validation (GV) Environment Canada (EC) Visibility Sensor FD12P C3VP dataset consists of visibility and precipitation data collected at the Environment Canada Canadian Climate station at the Centre for Atmospheric Research Experiments (CARE) site during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The GPM GV Visibility Sensor FD12P C3VP data are available from October 4, 2006 through March 31, 2007 in a Microsoft Excel comma-separated variable spreadsheet.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/94dbc271-66f3-496f-90f7-539c9652b67e","harvest_record_raw":"https://catalog.data.gov/harvest_record/94dbc271-66f3-496f-90f7-539c9652b67e/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPMGV/C3VP/VISSENSOR/DATA101","keyword":["earth-science-air-quality-atmosphere-visibility","earth-science-precipitation-atmosphere-liquid-precipitation"],"last_harvested_date":"2026-09-23T00:19:30.073523","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/MSFC/GHRC","slug":"gpm-ground-validation-environment-canada-ec-visibility-sensor-fd12p-c3vp-v1","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM Ground Validation Environment Canada (EC) Visibility Sensor FD12P C3VP V1","type":"dataset"},{"_score":19.831852,"_sort":[1790122727629,19.831852,1,"e1f92163-622e-43e9-a5bb-664a419a4589"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5. The data were produced by applying a Boosted Regression Tree machine learning approach to create gridded estimates of emissions based on in situ observations of NGS fluxes provided in a related dataset. The resulting monthly average flux data records can be used to calculate annual NGS soil CO2 flux budgets from 2003-2100.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2143812328-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://above.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/ABOVE/guides/Soil_Carbon_Flux_Maps_Fig1.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/Soil_Carbon_Flux_Maps_1683.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/above/Soil_Carbon_Flux_Maps/comp/Soil_Carbon_Flux_Maps.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/1683","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://opendap.earthdata.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2143812328-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/1683","keyword":["earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-frozen-ground-land-surface-permafrost","earth-science-soils-land-surface-carbon","earth-science-soils-land-surface-soil-respiration"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 89.979, \"EastBoundingCoordinate\": 179.896, \"SouthBoundingCoordinate\": -84.6916}]]","temporal":"1993-01-01/2100-11-30","theme":["Earth Science"],"title":"Gridded Winter Soil CO2 Flux Estimates for pan-Arctic and Boreal Regions, 2003-2100"},"description":"This dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5. The data were produced by applying a Boosted Regression Tree machine learning approach to create gridded estimates of emissions based on in situ observations of NGS fluxes provided in a related dataset. The resulting monthly average flux data records can be used to calculate annual NGS soil CO2 flux budgets from 2003-2100.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5ed8598b-70c2-4e85-abff-88258138a756","harvest_record_raw":"https://catalog.data.gov/harvest_record/5ed8598b-70c2-4e85-abff-88258138a756/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/1683","keyword":["earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-frozen-ground-land-surface-permafrost","earth-science-soils-land-surface-carbon","earth-science-soils-land-surface-soil-respiration"],"last_harvested_date":"2026-09-23T00:18:47.629389","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"gridded-winter-soil-co2-flux-estimates-for-pan-arctic-and-boreal-regions-2003-2100","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Gridded Winter Soil CO2 Flux Estimates for pan-Arctic and Boreal Regions, 2003-2100","type":"dataset"},{"_score":30.583317,"_sort":[1790122723239,30.583317,1,"451d38a9-2695-4448-9567-faf5f4d04cff"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Planetary Data System","hasEmail":"mailto:pds-operator@jpl.nasa.gov"},"description":"Unknown","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.mro-m-mcs-5-ddr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.mro-m-mcs-5-ddr-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Planetary Atmospheres"},"theme":["Planetary Science"],"title":"MRO MARS CLIMATE SOUNDER LEVEL 5 DDR V1.0"},"description":"Unknown","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/f9a5a381-0652-4087-8ea9-96d7621f35a9","harvest_record_raw":"https://catalog.data.gov/harvest_record/f9a5a381-0652-4087-8ea9-96d7621f35a9/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.mro-m-mcs-5-ddr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.mro-m-mcs-5-ddr-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-09-23T00:18:43.239278","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"Planetary Atmospheres","slug":"mro-mars-climate-sounder-level-5-ddr-v1-0-21a2a","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"MRO MARS CLIMATE SOUNDER LEVEL 5 DDR V1.0","type":"dataset"},{"_score":11.625898,"_sort":[1790122669646,11.625898,1,"add6a17b-671f-46db-b02c-d31535d917b5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The ISCCP_B3_NAT data is the International Satellite Cloud Climatology Project (ISCCP) Stage B3 Reduced Radiances in Native Format data product. This is the original radiance data, sampled to 30 Km and 3-hour spacing. Data collection for this product is complete and was collected using several instruments on multiple platforms, please see the instrument and platform list of this record for a comprehensive list. The normalization of all radiances to a standard calibration made these data a globally uniform set of measurements that can be used for detailed cloud process studies.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP) and was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to: produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nSince 1983 an international group of institutions has collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and inter-annual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onwards) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis is performed separately for each satellite radiance data set and the results reported in the Stage DX data product, which has a nominal resolution of 30 km and 3 hours. The Stage D1 product is produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product is produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3880704215-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/abstract_ISCCP_Stage_B3.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/isccp_project.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/isccp_b3_details.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_b3_kind.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_b3_module.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_b3_read.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_b3_read_linux.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/readme/readme_isccp_b3_nat.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/ISCCP","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://climatedataguide.ucar.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880704215-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ISCCP/B3_NAT","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://earthdata.nasa.gov/esds/competitive-programs/measures/data-analysis-for-isccp","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/ShadowsDoubt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/StarsCloudsCrops","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/docs/documents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/errors.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/newalg.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/products/products.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/aura/science-and-data/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880704215-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/learn/sensing-our-planet/but-the-winds-but-the-spaces","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.giss.nasa.gov/tools/panoply/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/ISCCP/B3_NAT","keyword":["earth-science-infrared-wavelengths-spectral-engineering-infrared-imagery","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-imagery","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"Boundary\": {\"Points\": [{\"Latitude\": -90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": -180}]}}]]","temporal":"1983-07-01/2009-12-31","theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage B3 Reduced Radiances in Native Format"},"description":"The ISCCP_B3_NAT data is the International Satellite Cloud Climatology Project (ISCCP) Stage B3 Reduced Radiances in Native Format data product. This is the original radiance data, sampled to 30 Km and 3-hour spacing. Data collection for this product is complete and was collected using several instruments on multiple platforms, please see the instrument and platform list of this record for a comprehensive list. The normalization of all radiances to a standard calibration made these data a globally uniform set of measurements that can be used for detailed cloud process studies.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP) and was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to: produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nSince 1983 an international group of institutions has collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and inter-annual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onwards) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis is performed separately for each satellite radiance data set and the results reported in the Stage DX data product, which has a nominal resolution of 30 km and 3 hours. The Stage D1 product is produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product is produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6d67fc18-0c4c-4b15-90ec-a4b6e2d75df0","harvest_record_raw":"https://catalog.data.gov/harvest_record/6d67fc18-0c4c-4b15-90ec-a4b6e2d75df0/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ISCCP/B3_NAT","keyword":["earth-science-infrared-wavelengths-spectral-engineering-infrared-imagery","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-imagery","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"last_harvested_date":"2026-09-23T00:17:49.646828","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"international-satellite-cloud-climatology-project-isccp-stage-b3-reduced-radiances-in-nati","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage B3 Reduced Radiances in Native Format","type":"dataset"},{"_score":12.258453,"_sort":[1790122669244,12.258453,1,"3af28914-9d66-4c1e-95ac-1657ee8803be"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The ISCCP_D2 data set contains monthly, 280 KM equal-area grid data from various polar and geostationary satellites. Climatological Summary Product contents contain monthly average of D1 quantities including mean diurnal cycle, distribution and properties of total cloudiness and cloud types.\r\n\r\nThe International Satellite Cloud Climatology Project (ISCCP), the first project of the World Climate Research Program (WCRP), was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983): \r\n- To produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived. \r\n- To stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models. \r\n- To promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nSince 1983 an international group of institutions has collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and interannual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onwards) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nThe ISCCP analysis combines satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determines the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis is performed separately for each satellite radiance data set and the results reported in the Stage DX data product, which has a nominal resolution of 30 km and 3 hours. The Stage D1 product is produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product is produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3880704243-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/base_isccp_d1_d2_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/isccp_project.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_d2_read.c","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_d2_read.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/readme/readme_isccp_d2.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/ISCCP","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://climatedataguide.ucar.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880704243-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ISCCP/D2","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://earthdata.nasa.gov/esds/competitive-programs/measures/data-analysis-for-isccp","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/ShadowsDoubt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/StarsCloudsCrops","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/docs/documents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/errors.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/newalg.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/products/products.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/aura/science-and-data/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880704243-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/learn/sensing-our-planet/but-the-winds-but-the-spaces","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.giss.nasa.gov/tools/panoply/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/ISCCP/D2","keyword":["earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species","earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-optical-depth-thickness","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-snow-ice-cryosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"Boundary\": {\"Points\": [{\"Latitude\": -90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": -180}]}}]]","temporal":"1983-07-01/2009-12-31","theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage D2 Monthly Cloud Products - Revised Algorithm in Hierarchical Data Format"},"description":"The ISCCP_D2 data set contains monthly, 280 KM equal-area grid data from various polar and geostationary satellites. Climatological Summary Product contents contain monthly average of D1 quantities including mean diurnal cycle, distribution and properties of total cloudiness and cloud types.\r\n\r\nThe International Satellite Cloud Climatology Project (ISCCP), the first project of the World Climate Research Program (WCRP), was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983): \r\n- To produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived. \r\n- To stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models. \r\n- To promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nSince 1983 an international group of institutions has collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and interannual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onwards) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nThe ISCCP analysis combines satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determines the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis is performed separately for each satellite radiance data set and the results reported in the Stage DX data product, which has a nominal resolution of 30 km and 3 hours. The Stage D1 product is produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product is produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0c7114e9-cf64-4d12-a671-4abd01286512","harvest_record_raw":"https://catalog.data.gov/harvest_record/0c7114e9-cf64-4d12-a671-4abd01286512/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ISCCP/D2","keyword":["earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species","earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-optical-depth-thickness","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-snow-ice-cryosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover"],"last_harvested_date":"2026-09-23T00:17:49.244521","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"international-satellite-cloud-climatology-project-isccp-stage-d2-monthly-cloud-products-re","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage D2 Monthly Cloud Products - Revised Algorithm in Hierarchical Data Format","type":"dataset"},{"_score":11.623006,"_sort":[1790122668845,11.623006,1,"4cfd56fb-eafd-4d45-83ba-ce7a0edc7d20"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"ISCCP_DX_1 is the International Satellite Cloud Climatology Project (ISCCP) Stage DX Pixel Level Cloud Product - Revised Algorithm in Binary Format data set. It contains 3-hourly, 30 KM satellite image projection data from various polar and geostationary satellites. Pixel Level Cloud Product contents include calibrated radiances, cloud detection results, and cloud and surface properties from radiative analysis. Data collection for this data set is complete.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP) and was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to:\r\nproduce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle.\r\n\r\nStarting in 1983, an international group of institutions collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and inter-annual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onward) the analysis was also concerned with improving understanding of clouds in the global hydrological cycle.\r\n\r\nThe ISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985, Rossow et al. 1987) with the Tiros Operational Vertical Sounder (TOVS) atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of or absence of clouds in each individual image pixel and retrieved the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis was performed separately for each satellite radiance data set and the results were reported in the Stage DX data product, which had a nominal resolution of 30 km and 3 hours. The Stage D1 product was produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product was produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3880704255-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/base_isccp_dx_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/isccp_project.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_dx_read.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/linux_isccp_dx_read.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/readme/readme_isccp_dx_nat.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/ISCCP","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://climatedataguide.ucar.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880704255-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ISCCP/DX","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://earthdata.nasa.gov/esds/competitive-programs/measures/data-analysis-for-isccp","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/ShadowsDoubt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/StarsCloudsCrops","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/docs/documents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/errors.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/newalg.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/products/products.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/aura/science-and-data/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880704255-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/learn/sensing-our-planet/but-the-winds-but-the-spaces","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.giss.nasa.gov/tools/panoply/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/ISCCP/DX","keyword":["earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-infrared-wavelengths-spectral-engineering-infrared-imagery","earth-science-visible-wavelengths-spectral-engineering-visible-imagery"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"Boundary\": {\"Points\": [{\"Latitude\": -90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": -180}]}}]]","temporal":"1983-06-30/2009-12-31","theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage DX Pixel Level Cloud Product - Revised Algorithm in Binary Format"},"description":"ISCCP_DX_1 is the International Satellite Cloud Climatology Project (ISCCP) Stage DX Pixel Level Cloud Product - Revised Algorithm in Binary Format data set. It contains 3-hourly, 30 KM satellite image projection data from various polar and geostationary satellites. Pixel Level Cloud Product contents include calibrated radiances, cloud detection results, and cloud and surface properties from radiative analysis. Data collection for this data set is complete.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP) and was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to:\r\nproduce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle.\r\n\r\nStarting in 1983, an international group of institutions collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and inter-annual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onward) the analysis was also concerned with improving understanding of clouds in the global hydrological cycle.\r\n\r\nThe ISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985, Rossow et al. 1987) with the Tiros Operational Vertical Sounder (TOVS) atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of or absence of clouds in each individual image pixel and retrieved the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis was performed separately for each satellite radiance data set and the results were reported in the Stage DX data product, which had a nominal resolution of 30 km and 3 hours. The Stage D1 product was produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product was produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/318b868e-b8f7-43fe-b1de-7d6a49039587","harvest_record_raw":"https://catalog.data.gov/harvest_record/318b868e-b8f7-43fe-b1de-7d6a49039587/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ISCCP/DX","keyword":["earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-infrared-wavelengths-spectral-engineering-infrared-imagery","earth-science-visible-wavelengths-spectral-engineering-visible-imagery"],"last_harvested_date":"2026-09-23T00:17:48.845462","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"international-satellite-cloud-climatology-project-isccp-stage-dx-pixel-level-cloud-product","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage DX Pixel Level Cloud Product - Revised Algorithm in Binary Format","type":"dataset"},{"_score":59.01831,"_sort":[1790122667750,59.01831,1,"3dd81fdf-7297-4dfe-8f30-dac14345b3d7"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This data set contains a mean monthly climatology for several climate variables averaged over the period from 1961 to 1990,  and constructed from a data set of station 1961-1990 climatological normals, numbering between 19,800 (precipitation) and 3,615 (windspeed; see New et al, 1999 for details). The station data were interpolated as a function of latitude, longitude and elevation using thin-plate splines. The data comprise a suite of climate elements: precipitation, mean, maximum, and minimum  temperature, frost frequency, diurnal temperature range, radiation, wet-day frequency, vapor pressure, wind, and cloud cover. There are 23 files in this data set provided at 0.5 and 1.0 degree spatial resolutions.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2785293782-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/1015_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/cru_monthly_mean_xdeg_1015.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/near-surface_met/cru_monthly_mean_xdeg/comp/0_cru_mean_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/near-surface_met/cru_monthly_mean_xdeg/comp/1_cru_monthly_mean_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/1015","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2785293782-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/1015","keyword":["earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-clouds-atmosphere-cloud-properties","earth-science-precipitation-atmosphere-precipitation-amount","earth-science-topography-land-surface-terrain-elevation"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1986-01-01/1995-12-31","theme":["Earth Science"],"title":"ISLSCP II Climate Research Unit CRU05 Monthly Climate Data"},"description":"This data set contains a mean monthly climatology for several climate variables averaged over the period from 1961 to 1990,  and constructed from a data set of station 1961-1990 climatological normals, numbering between 19,800 (precipitation) and 3,615 (windspeed; see New et al, 1999 for details). The station data were interpolated as a function of latitude, longitude and elevation using thin-plate splines. The data comprise a suite of climate elements: precipitation, mean, maximum, and minimum  temperature, frost frequency, diurnal temperature range, radiation, wet-day frequency, vapor pressure, wind, and cloud cover. There are 23 files in this data set provided at 0.5 and 1.0 degree spatial resolutions.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e68efab6-fae6-4705-afd4-c20b935c7747","harvest_record_raw":"https://catalog.data.gov/harvest_record/e68efab6-fae6-4705-afd4-c20b935c7747/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/1015","keyword":["earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-clouds-atmosphere-cloud-properties","earth-science-precipitation-atmosphere-precipitation-amount","earth-science-topography-land-surface-terrain-elevation"],"last_harvested_date":"2026-09-23T00:17:47.750117","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"islscp-ii-climate-research-unit-cru05-monthly-climate-data","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II Climate Research Unit CRU05 Monthly Climate Data","type":"dataset"},{"_score":8.876841,"_sort":[1790122667037,8.876841,1,"28c58c69-0b6e-48df-a9a7-f02ee3d4b2ba"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types. For tree cover, additional fields describing leaf longevity (evergreen and deciduous) and leaf morphology (broadleaf and needleleaf) were also generated. The modeling of carbon dynamics and climate require knowing tree characteristics such as these. These products were resampled and aggregated to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. The data set describes the geographic distributions of three fundamental vegetation characteristics: tree, herbaceous and bare ground cover, plus a water layer. For tree cover, leaf longevity and morphology layers were produced.\n\nThis data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.\n\nISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784863182-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/931_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/veg_continuous_fields_xdeg_931.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/veg_continuous_fields_xdeg/comp/0_veg_cont_fields_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/veg_continuous_fields_xdeg/comp/1_veg_continuous_fields_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/931","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784863182-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/931","keyword":["earth-science-atmospheric-radiation-atmosphere-albedo","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1992-04-01/1993-05-31","theme":["Earth Science"],"title":"ISLSCP II Continuous Fields of Vegetation Cover, 1992-1993"},"description":"The objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types. For tree cover, additional fields describing leaf longevity (evergreen and deciduous) and leaf morphology (broadleaf and needleleaf) were also generated. The modeling of carbon dynamics and climate require knowing tree characteristics such as these. These products were resampled and aggregated to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. The data set describes the geographic distributions of three fundamental vegetation characteristics: tree, herbaceous and bare ground cover, plus a water layer. For tree cover, leaf longevity and morphology layers were produced.\n\nThis data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.\n\nISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c4f613a6-f63e-42f1-afd6-6a1f48ed251c","harvest_record_raw":"https://catalog.data.gov/harvest_record/c4f613a6-f63e-42f1-afd6-6a1f48ed251c/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/931","keyword":["earth-science-atmospheric-radiation-atmosphere-albedo","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:17:47.037991","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"islscp-ii-continuous-fields-of-vegetation-cover-1992-1993","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II Continuous Fields of Vegetation Cover, 1992-1993","type":"dataset"},{"_score":11.614737,"_sort":[1790122665932,11.614737,1,"5c4879bb-7784-4f31-b137-259eddb40aaf"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. FASIR-NDVI data are also used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface. There are three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude. FASIR adjustments concentrated on reducing NDVI variations arising from atmospheric, calibration, view and illumination geometries and other effects not related to actual vegetation change.\n\nFASIR NDVI was also generated to provide inputs for computing a 17-year time series of associated biophysical parameters, provided as a separate data set in this data collection. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784893177-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/972_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/fasir_ndvi_monthly_xdeg_972.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/fasir_ndvi_monthly_xdeg/comp/0_fasir_ndvi_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/fasir_ndvi_monthly_xdeg/comp/1_fasir_ndvi_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/972","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784893177-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/972","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1982-01-01/1998-12-31","theme":["Earth Science"],"title":"ISLSCP II FASIR-adjusted NDVI, 1982-1998"},"description":"The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. FASIR-NDVI data are also used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface. There are three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude. FASIR adjustments concentrated on reducing NDVI variations arising from atmospheric, calibration, view and illumination geometries and other effects not related to actual vegetation change.\n\nFASIR NDVI was also generated to provide inputs for computing a 17-year time series of associated biophysical parameters, provided as a separate data set in this data collection. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f88ca682-0edc-4ac6-b657-7fd0e07f425e","harvest_record_raw":"https://catalog.data.gov/harvest_record/f88ca682-0edc-4ac6-b657-7fd0e07f425e/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/972","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:17:45.932678","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"islscp-ii-fasir-adjusted-ndvi-1982-1998","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II FASIR-adjusted NDVI, 1982-1998","type":"dataset"},{"_score":11.576915,"_sort":[1790122665199,11.576915,2,"1040dd4a-73ba-432e-a209-de8d923d3f23"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 22-year satellite record of monthly changes in terrestrial vegetation. This data set contains three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude with data from July 1981 through December 2002. New features include reduced NDVI variations arising from calibration, view geometry, volcanic aerosols, and other effects not related to actual vegetation change. In particular, NOAA-9 descending node data from September 1994 to January 1995, volcanic stratospheric aerosol correction for 1982-1984 and 1991-1994, and improved NDVI using empirical mode decomposition/reconstruction (EMD) to minimize effects of orbital drift. Global NDVI was generated to provide inputs for computing the time series of biophysical parameters contained in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II collection. NDVI is used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784893549-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/973_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/gimms_ndvi_monthly_xdeg_973.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/gimms_ndvi_monthly_xdeg/comp/0_gimms_ndvi_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/gimms_ndvi_monthly_xdeg/comp/1_gimms_ndvi_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/973","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784893549-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/973","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1981-07-01/2002-12-31","theme":["Earth Science"],"title":"ISLSCP II GIMMS Monthly NDVI, 1981-2002"},"description":"The Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 22-year satellite record of monthly changes in terrestrial vegetation. This data set contains three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude with data from July 1981 through December 2002. New features include reduced NDVI variations arising from calibration, view geometry, volcanic aerosols, and other effects not related to actual vegetation change. In particular, NOAA-9 descending node data from September 1994 to January 1995, volcanic stratospheric aerosol correction for 1982-1984 and 1991-1994, and improved NDVI using empirical mode decomposition/reconstruction (EMD) to minimize effects of orbital drift. Global NDVI was generated to provide inputs for computing the time series of biophysical parameters contained in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II collection. NDVI is used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/667a66ed-0d6b-4393-b417-5a878f424605","harvest_record_raw":"https://catalog.data.gov/harvest_record/667a66ed-0d6b-4393-b417-5a878f424605/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/973","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:17:45.199172","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"ORNL_DAAC","slug":"islscp-ii-gimms-monthly-ndvi-1981-2002","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II GIMMS Monthly NDVI, 1981-2002","type":"dataset"},{"_score":8.180333,"_sort":[1790122662289,8.180333,2,"9de5d5ab-6430-44c4-bc28-e3491252a6cf"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data set, ISLSCP II Global Sea Ice Concentration, is based on the Goddard Space Flight Center (GSFC) Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellites Program (DMSP) Special Sensor Microwave/Imager (SSM/I) Passive Microwave Data. This data set contains four zip files which includes sea ice concentration (in percentage of ocean area covered by sea ice), table data and map data.  These original data were re-gridded by the National Snow and Ice Data Center (NSIDC) from their original 25-km spatial resolution and EASE-Grid into equal angle Earth grids with quarter, half and one degree spatial resolutions in latitude/longitude. The ISLSCP II staff have taken the one degree resolution original data provided by the Principal Investigator and created global maps of monthly sea ice concentration on a global one degree grid using the latitude and longitude coordinates that were provided. Individual monthly files were created and written to the ASCII format. The re-gridded one degree original data were also adjusted to match the one degree ISLSCP II land/water mask.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784896705-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/981_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/sea_ice_extent_xdeg_981.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/snow_sea-ice_oceans/sea_ice_extent_xdeg/comp/0_sea_ice_extent_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/snow_sea-ice_oceans/sea_ice_extent_xdeg/comp/1_sea_ice_extent_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/981","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784896705-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/981","keyword":["earth-science-cryospheric-indicators-climate-indicators-sea-ice-concentration","earth-science-sea-ice-cryosphere-sea-ice-concentration","earth-science-sea-ice-oceans-ice-extent","earth-science-sea-ice-oceans-sea-ice-concentration"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1986-01-01/1995-12-31","theme":["Earth Science"],"title":"ISLSCP II Global Sea Ice Concentration"},"description":"This International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data set, ISLSCP II Global Sea Ice Concentration, is based on the Goddard Space Flight Center (GSFC) Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellites Program (DMSP) Special Sensor Microwave/Imager (SSM/I) Passive Microwave Data. This data set contains four zip files which includes sea ice concentration (in percentage of ocean area covered by sea ice), table data and map data.  These original data were re-gridded by the National Snow and Ice Data Center (NSIDC) from their original 25-km spatial resolution and EASE-Grid into equal angle Earth grids with quarter, half and one degree spatial resolutions in latitude/longitude. The ISLSCP II staff have taken the one degree resolution original data provided by the Principal Investigator and created global maps of monthly sea ice concentration on a global one degree grid using the latitude and longitude coordinates that were provided. Individual monthly files were created and written to the ASCII format. The re-gridded one degree original data were also adjusted to match the one degree ISLSCP II land/water mask.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1f288c24-6c98-4c15-bd95-13d79db0d5a7","harvest_record_raw":"https://catalog.data.gov/harvest_record/1f288c24-6c98-4c15-bd95-13d79db0d5a7/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/981","keyword":["earth-science-cryospheric-indicators-climate-indicators-sea-ice-concentration","earth-science-sea-ice-cryosphere-sea-ice-concentration","earth-science-sea-ice-oceans-ice-extent","earth-science-sea-ice-oceans-sea-ice-concentration"],"last_harvested_date":"2026-09-23T00:17:42.289509","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"ORNL_DAAC","slug":"islscp-ii-global-sea-ice-concentration","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II Global Sea Ice Concentration","type":"dataset"},{"_score":13.744064,"_sort":[1790122661170,13.744064,4,"ff9f54e8-f5ef-4cab-8f3e-417b23f4a04c"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Historical Croplands Cover data set was developed to understand the consequences of historical changes in land use and land cover for ecosystem goods and services. In particular, this data set can be used to study how global changes in cultivated area has influenced climate, biogeochemical cycles, biodiversity, etc. This data set can be used directly within spatially-explicit climate and biogeochemical models.\r\n\r\nThis is a gridded data set describing the fraction of each grid cell in the globe that is occupied by cultivated land from 1700 to 1992. Data layers are provided for every 50 years from 1700 to 1850, every 10 years from 1850 to 1980, and every year from 1986 to 1992.\r\n\r\nThere are two sources of global land cover/land use data. The most recent estimates are derived from satellite measurements, and are available in a spatially-explicit fashion for roughly the last 30 years. The other estimate is based on ground-based sources such as census statistics, land surveys, estimates by historical geographers, etc. These land inventory data are only available at the scale of political units, but have the advantage of being historical. Ramankutty and Foley (1998) derived a spatially-explicit data set of croplands in 1992 by synthesizing remotely-sensed land cover data with contemporary land inventory data. Furthermore, Ramankutty and Foley (1999) extended this data set into the past (back to 1700) using historical land inventory data.\r\n\r\nThe data set should only be used for continental-to-global scale analysis and modeling. The data set captures the broad patterns of cropland change over history, but not necessarily the fine details at local to regional scales - please check the data quality before using it at fine spatial scales. The quality of historical data for the Russian Federation is poor. The quality of data prior to 1850 is poor -- only continental-scale historical data were used for that period.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784888334-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/966_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/historic_cropland_xdeg_966.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/historic_cropland_xdeg/comp/0_historic_cropland_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/historic_cropland_xdeg/comp/1_historic_cropland_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/966","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784888334-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/966","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1700-01-01/1992-12-31","theme":["Earth Science"],"title":"ISLSCP II Historical Croplands Cover, 1700-1992"},"description":"The Historical Croplands Cover data set was developed to understand the consequences of historical changes in land use and land cover for ecosystem goods and services. In particular, this data set can be used to study how global changes in cultivated area has influenced climate, biogeochemical cycles, biodiversity, etc. This data set can be used directly within spatially-explicit climate and biogeochemical models.\r\n\r\nThis is a gridded data set describing the fraction of each grid cell in the globe that is occupied by cultivated land from 1700 to 1992. Data layers are provided for every 50 years from 1700 to 1850, every 10 years from 1850 to 1980, and every year from 1986 to 1992.\r\n\r\nThere are two sources of global land cover/land use data. The most recent estimates are derived from satellite measurements, and are available in a spatially-explicit fashion for roughly the last 30 years. The other estimate is based on ground-based sources such as census statistics, land surveys, estimates by historical geographers, etc. These land inventory data are only available at the scale of political units, but have the advantage of being historical. Ramankutty and Foley (1998) derived a spatially-explicit data set of croplands in 1992 by synthesizing remotely-sensed land cover data with contemporary land inventory data. Furthermore, Ramankutty and Foley (1999) extended this data set into the past (back to 1700) using historical land inventory data.\r\n\r\nThe data set should only be used for continental-to-global scale analysis and modeling. The data set captures the broad patterns of cropland change over history, but not necessarily the fine details at local to regional scales - please check the data quality before using it at fine spatial scales. The quality of historical data for the Russian Federation is poor. The quality of data prior to 1850 is poor -- only continental-scale historical data were used for that period.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d25a4da0-2e22-4733-86e0-d87e6ccb376c","harvest_record_raw":"https://catalog.data.gov/harvest_record/d25a4da0-2e22-4733-86e0-d87e6ccb376c/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/966","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:17:41.170485","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":4,"publisher":"ORNL_DAAC","slug":"islscp-ii-historical-croplands-cover-1700-1992","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II Historical Croplands Cover, 1700-1992","type":"dataset"},{"_score":12.251391,"_sort":[1790122660813,12.251391,1,"1a79fd18-1e6a-4cb7-91db-78778c5b416d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set describes historical land use changes over a 300-year historical period (1700-1990).\n\nTesting against historical data is an important step for validating integrated models of global environmental change. Owing to long time lags in the climate and biogeochemical systems, these models should aim to simulate the land use dynamics for long periods, i.e., spanning decades to centuries. Developing such models requires an understanding of past and current trends and is therefore strongly data dependent. For this purpose, a historical database of the global environment has been developed: HYDE. Historical statistical inventories on agricultural land (census data, tax records, land surveys, etc) and different spatial analysis techniques were used to create a geographically-explicit data set of land use change, with a regular time interval. The data set can be used to test integrated models of global change. Continental-scale historical data were used for that period.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784888644-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/967_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/historic_landcover_xdeg_967.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/historic_landcover_xdeg/comp/0_historic_landcover_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/historic_landcover_xdeg/comp/1_historic_landcover_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/967","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784888644-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/967","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1700-01-01/1990-12-31","theme":["Earth Science"],"title":"ISLSCP II Historical Land Cover and Land Use, 1700-1990"},"description":"The Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set describes historical land use changes over a 300-year historical period (1700-1990).\n\nTesting against historical data is an important step for validating integrated models of global environmental change. Owing to long time lags in the climate and biogeochemical systems, these models should aim to simulate the land use dynamics for long periods, i.e., spanning decades to centuries. Developing such models requires an understanding of past and current trends and is therefore strongly data dependent. For this purpose, a historical database of the global environment has been developed: HYDE. Historical statistical inventories on agricultural land (census data, tax records, land surveys, etc) and different spatial analysis techniques were used to create a geographically-explicit data set of land use change, with a regular time interval. The data set can be used to test integrated models of global change. Continental-scale historical data were used for that period.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6d8dd9fa-b65a-4542-870b-fa017f99f97e","harvest_record_raw":"https://catalog.data.gov/harvest_record/6d8dd9fa-b65a-4542-870b-fa017f99f97e/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/967","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-albedo","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-canopy-characteristics","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-plant-characteristics","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:17:40.813471","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"islscp-ii-historical-land-cover-and-land-use-1700-1990","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II Historical Land Cover and Land Use, 1700-1990","type":"dataset"},{"_score":11.2865925,"_sort":[1790122641757,11.2865925,2,"6afe261f-ba7e-4d02-a1f6-b3968c01e98b"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries. One-time estimates of aboveground carbon and soil organic carbon stocks for the PMFM derived from existing sources are provided. Also included are the monthly mean normalized difference vegetation index (NDVI) from MOD13A3 used to derive greenness trends, monthly mean air temperature, and total monthly precipitation from Daymet for 2001-2015 across the PMFM. Other files include the distribution and coverage of PMFM across Mexico. Distributions are provided as four categories of PMFM: (1) Arid mangroves with Surface Water as main input, along the Gulf of California and Pacific Coast (ARsw); (2) humid mangroves with surface water input along the Pacific Coast (HUsw-Pa); (3) humid mangroves with surface water input along the coast of the Gulf of Mexico (HUsw-Gf); (4) humid mangroves with groundwater input along the Gulf of Mexico and Caribbean Sea (HUgw). These data provide a baseline for national monitoring programs, carbon accounting models, and greenness trends in coastal wetlands.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2345881744-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/CMS/guides/CStocks_Greenness_Mangroves_MX_Fig1.jpg","format":"JPEG","mediaType":"image/jpeg"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/CStocks_Greenness_Mangroves_MX_1853.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/cms/CStocks_Greenness_Mangroves_MX/comp/CStocks_Greenness_Mangroves_MX.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/1853","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2345881744-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/1853","keyword":["earth-science-ecosystems-biosphere-marine-ecosystems","earth-science-land-use-land-cover-land-surface","earth-science-soils-land-surface-carbon","earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -114.055, \"NorthBoundingCoordinate\": 29.7262, \"EastBoundingCoordinate\": -86.5302, \"SouthBoundingCoordinate\": 14.4292}]]","temporal":"2001-01-01/2015-12-31","theme":["Earth Science"],"title":"Greenness Trends and Carbon Stocks of Mangrove Forests Across Mexico, 2001-2015"},"description":"This dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries. One-time estimates of aboveground carbon and soil organic carbon stocks for the PMFM derived from existing sources are provided. Also included are the monthly mean normalized difference vegetation index (NDVI) from MOD13A3 used to derive greenness trends, monthly mean air temperature, and total monthly precipitation from Daymet for 2001-2015 across the PMFM. Other files include the distribution and coverage of PMFM across Mexico. Distributions are provided as four categories of PMFM: (1) Arid mangroves with Surface Water as main input, along the Gulf of California and Pacific Coast (ARsw); (2) humid mangroves with surface water input along the Pacific Coast (HUsw-Pa); (3) humid mangroves with surface water input along the coast of the Gulf of Mexico (HUsw-Gf); (4) humid mangroves with groundwater input along the Gulf of Mexico and Caribbean Sea (HUgw). These data provide a baseline for national monitoring programs, carbon accounting models, and greenness trends in coastal wetlands.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/fdb20d88-8e18-43d4-a4d1-bcd765e7a40b","harvest_record_raw":"https://catalog.data.gov/harvest_record/fdb20d88-8e18-43d4-a4d1-bcd765e7a40b/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/1853","keyword":["earth-science-ecosystems-biosphere-marine-ecosystems","earth-science-land-use-land-cover-land-surface","earth-science-soils-land-surface-carbon","earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:17:21.757413","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"ORNL_DAAC","slug":"greenness-trends-and-carbon-stocks-of-mangrove-forests-across-mexico-2001-2015","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Greenness Trends and Carbon Stocks of Mangrove Forests Across Mexico, 2001-2015","type":"dataset"},{"_score":15.599131,"_sort":[1790122631000,15.599131,1,"5841b7e6-dc31-4038-89ee-5225047e42ec"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This data set was developed to describe the state of the global land cover in terms of 15 major vegetation types, plus water, before alteration by humans. It forms a complement to the historical croplands data set developed by Ramankutty and Foley (1999). By overlaying the two, one can determine the extent to which natural vegetation has been cleared for cultivation. This data set can be used directly within spatially-explicit climate and biogeochemical models. There are four total files in this data set. Two files contain the land cover types representing potential natural vegetation before human alteration, and two other files contain those points in the original data set submitted by the Principal Investigator that have been modified in order to match the land/water mask of the ISLSCP Initiative II.\r\n\r\nThe geographic distribution of contemporary land cover types can be derived from remotely-sensed data. However, humans now dominate much of the world and there is little evidence of the pre-human-settlement natural vegetation or Potential Natural Vegetation (PNV). PNV, as defined here, does not necessarily represent the world'??s natural pre-human-disturbance vegetation. Rather, our definition of PNV represents the world's vegetation cover that would most likely exist now in equilibrium with present-day climate and natural disturbance, in the absence of human activities.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784887174-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/961_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/potential_veg_xdeg_961.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/potential_veg_xdeg/comp/0_potential_veg_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/vegetation/potential_veg_xdeg/comp/1_potential_veg_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/961","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784887174-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/961","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-deciduous-vegetation","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1992-04-01/1993-04-01","theme":["Earth Science"],"title":"ISLSCP II Potential Natural Vegetation Cover"},"description":"This data set was developed to describe the state of the global land cover in terms of 15 major vegetation types, plus water, before alteration by humans. It forms a complement to the historical croplands data set developed by Ramankutty and Foley (1999). By overlaying the two, one can determine the extent to which natural vegetation has been cleared for cultivation. This data set can be used directly within spatially-explicit climate and biogeochemical models. There are four total files in this data set. Two files contain the land cover types representing potential natural vegetation before human alteration, and two other files contain those points in the original data set submitted by the Principal Investigator that have been modified in order to match the land/water mask of the ISLSCP Initiative II.\r\n\r\nThe geographic distribution of contemporary land cover types can be derived from remotely-sensed data. However, humans now dominate much of the world and there is little evidence of the pre-human-settlement natural vegetation or Potential Natural Vegetation (PNV). PNV, as defined here, does not necessarily represent the world'??s natural pre-human-disturbance vegetation. Rather, our definition of PNV represents the world's vegetation cover that would most likely exist now in equilibrium with present-day climate and natural disturbance, in the absence of human activities.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b2511173-6f20-41c5-9fe2-d517e83d0588","harvest_record_raw":"https://catalog.data.gov/harvest_record/b2511173-6f20-41c5-9fe2-d517e83d0588/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/961","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-surface-radiative-properties-land-surface-reflectance","earth-science-vegetation-biosphere-deciduous-vegetation","earth-science-vegetation-biosphere-vegetation-cover","earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:17:11.000862","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"islscp-ii-potential-natural-vegetation-cover","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II Potential Natural Vegetation Cover","type":"dataset"},{"_score":14.388378,"_sort":[1790122629903,14.388378,2,"c4ed95f1-f4ae-4a04-9a42-1148af6245f7"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling). Such a data assimilation scheme preserves the spatial specificity of the water balance calculations while constrained by the more accurate discharge measurement. There are 11 data files in this data set and 1 changemap file which shows the differences between the ISLSCP II land/water mask and the original data set.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784897571-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/994_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/comp_runoff_monthly_xdeg_994.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/hydrology_soils/comp_runoff_monthly_xdeg/comp/0_comp_runoff_month_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/islscp_ii/hydrology_soils/comp_runoff_monthly_xdeg/comp/1_comp_runoff_month_doc.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/994","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784897571-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/994","keyword":["earth-science-ecological-dynamics-biosphere-ecosystem-functions","earth-science-ecosystems-biosphere-terrestrial-ecosystems","earth-science-topography-land-surface-topographic-effects"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180.0, \"NorthBoundingCoordinate\": 90.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]","temporal":"1986-01-01/1995-12-31","theme":["Earth Science"],"title":"ISLSCP II UNH/GRDC Composite Monthly Runoff"},"description":"The University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling). Such a data assimilation scheme preserves the spatial specificity of the water balance calculations while constrained by the more accurate discharge measurement. There are 11 data files in this data set and 1 changemap file which shows the differences between the ISLSCP II land/water mask and the original data set.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f24468cb-877e-4c76-a456-f042e729522c","harvest_record_raw":"https://catalog.data.gov/harvest_record/f24468cb-877e-4c76-a456-f042e729522c/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/994","keyword":["earth-science-ecological-dynamics-biosphere-ecosystem-functions","earth-science-ecosystems-biosphere-terrestrial-ecosystems","earth-science-topography-land-surface-topographic-effects"],"last_harvested_date":"2026-09-23T00:17:09.903628","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"ORNL_DAAC","slug":"islscp-ii-unh-grdc-composite-monthly-runoff","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ISLSCP II UNH/GRDC Composite Monthly Runoff","type":"dataset"},{"_score":7.8486943,"_sort":[1790122628444,7.8486943,1,"555e1a5c-631a-4266-8538-a29ebdce1d5b"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GPM Ground Validation Airborne Snow Observatory (ASO) OLYMPEX dataset consists of snow depth, bare earth surface, land surface classification and a Red, Green, Blue (RGB) composite  image, provided at 3 m spatial resolution during the GPM Ground Validation Olympic Mountains Experiment (OLYMPEX) field campaign held in the Pacific Northwest. These data were collected by a Riegl Q1560 scanning LiDAR and an ITRES CASI-1500 imaging spectrometer , both part of the NASA Airborne Snow Observatory (ASO), during two separate periods, February 8-9, 2016 and March 29-30, 2016.  A previous September 2014 flight was used to obtain no-snow measurements used for deriving snow depth. The data are provided in GeoTIFF format.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1979129209-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.5067/GPMGV/OLYMPEX/DATA101","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"http://olympex.atmos.washington.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1016/j.rse.2016.06.018","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/JHM-D-17-0076.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://dx.doi.org/10.1109/TGRS.2006.890412","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://dx.doi.org/10.1175/BAMS-D-16-0182.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmasoolyx__1/docs/gpmasoolyx_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/field-campaigns/olympex","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1979129209-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPMGV/OLYMPEX/LIDAR/DATA101","keyword":["earth-science-cryospheric-indicators-climate-indicators-snow-depth","earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-topography-land-surface-terrain-elevation","earth-science-visible-wavelengths-spectral-engineering-visible-imagery"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -124.185, \"NorthBoundingCoordinate\": 48.0909, \"EastBoundingCoordinate\": -123.129, \"SouthBoundingCoordinate\": 47.493}]]","temporal":"2014-09-04/2016-03-30","theme":["Earth Science"],"title":"GPM Ground Validation Airborne Snow Observatory (ASO) OLYMPEX V1"},"description":"The GPM Ground Validation Airborne Snow Observatory (ASO) OLYMPEX dataset consists of snow depth, bare earth surface, land surface classification and a Red, Green, Blue (RGB) composite  image, provided at 3 m spatial resolution during the GPM Ground Validation Olympic Mountains Experiment (OLYMPEX) field campaign held in the Pacific Northwest. These data were collected by a Riegl Q1560 scanning LiDAR and an ITRES CASI-1500 imaging spectrometer , both part of the NASA Airborne Snow Observatory (ASO), during two separate periods, February 8-9, 2016 and March 29-30, 2016.  A previous September 2014 flight was used to obtain no-snow measurements used for deriving snow depth. The data are provided in GeoTIFF format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/92b35bcb-8a6e-4322-bf37-655a576146ad","harvest_record_raw":"https://catalog.data.gov/harvest_record/92b35bcb-8a6e-4322-bf37-655a576146ad/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPMGV/OLYMPEX/LIDAR/DATA101","keyword":["earth-science-cryospheric-indicators-climate-indicators-snow-depth","earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-topography-land-surface-terrain-elevation","earth-science-visible-wavelengths-spectral-engineering-visible-imagery"],"last_harvested_date":"2026-09-23T00:17:08.444989","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/MSFC/GHRC","slug":"gpm-ground-validation-airborne-snow-observatory-aso-olympex-v1","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM Ground Validation Airborne Snow Observatory (ASO) OLYMPEX V1","type":"dataset"},{"_score":13.337899,"_sort":[1790122625038,13.337899,1,"8e35d43d-30a6-4a25-8cd1-0aec49e6ac12"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GPM Ground Validation CAX1 Radar CFradial format OLYMPEX dataset consists of radar parameters, such as Radar reflectivity, Doppler velocity, Doppler width, Differential reflectivity, and Signal quality index, provided on a 0.4 to 1.0 km spatial resolution within the OLYMPEX field campaign study region in the state of Washington. These data were obtained for the GPM Ground Validation OLYMPEX field campaign by the SELEX Meteor 60DX10 Compact Weather (CAX1) radar. The CAX1 radar was located at the southern tip of Vancouver Island on the Canadian Forces Base (CFB) Esquimalt Albert Head (AHD) military training area. The CAX1 radar was operated by Environment and Climate Change Canada to support the OLYMPEX field campaign. These data are available in Cfradial netCDF-4 format from November 14, 2015 through April 1, 2016.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1981360835-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.5067/GPMGV/OLYMPEX/DATA101","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"http://olympex.atmos.washington.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ams.confex.com/ams/17Mountain/webprogram/Paper296448.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://dx.doi.org/10.1175/BAMS-D-16-0182.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmcax1cfolyx__1/docs/gpmcax1cfolyx_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/field-campaigns/olympex","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1981360835-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPMGV/OLYMPEX/XBAND/DATA301","keyword":["earth-science-radar-spectral-engineering-doppler-velocity","earth-science-radar-spectral-engineering-radar-reflectivity","earth-science-radar-spectral-engineering-spectrum-width"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -124.376, \"NorthBoundingCoordinate\": 49.2854, \"EastBoundingCoordinate\": -122.58, \"SouthBoundingCoordinate\": 47.4888}]]","temporal":"2015-11-14/2016-04-01","theme":["Earth Science"],"title":"GPM Ground Validation CAX1 Radar CFradial format OLYMPEX V1"},"description":"The GPM Ground Validation CAX1 Radar CFradial format OLYMPEX dataset consists of radar parameters, such as Radar reflectivity, Doppler velocity, Doppler width, Differential reflectivity, and Signal quality index, provided on a 0.4 to 1.0 km spatial resolution within the OLYMPEX field campaign study region in the state of Washington. These data were obtained for the GPM Ground Validation OLYMPEX field campaign by the SELEX Meteor 60DX10 Compact Weather (CAX1) radar. The CAX1 radar was located at the southern tip of Vancouver Island on the Canadian Forces Base (CFB) Esquimalt Albert Head (AHD) military training area. The CAX1 radar was operated by Environment and Climate Change Canada to support the OLYMPEX field campaign. These data are available in Cfradial netCDF-4 format from November 14, 2015 through April 1, 2016.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/41e8a2a6-25a7-4a2d-ac96-b5c953866ead","harvest_record_raw":"https://catalog.data.gov/harvest_record/41e8a2a6-25a7-4a2d-ac96-b5c953866ead/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPMGV/OLYMPEX/XBAND/DATA301","keyword":["earth-science-radar-spectral-engineering-doppler-velocity","earth-science-radar-spectral-engineering-radar-reflectivity","earth-science-radar-spectral-engineering-spectrum-width"],"last_harvested_date":"2026-09-23T00:17:05.038479","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/MSFC/GHRC","slug":"gpm-ground-validation-cax1-radar-cfradial-format-olympex-v1","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM Ground Validation CAX1 Radar CFradial format OLYMPEX V1","type":"dataset"},{"_score":6.1792574,"_sort":[1790122622106,6.1792574,1,"cf2d6699-7ec6-48d3-90b7-4d2b0871ba8a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GPM Ground Validation Environment Canada (EC) Weather Station XET C3VP dataset consists of surface meteorological data collected at the Environment Canada (EC) XET station at the Centre for Atmospheric Research Experiments (CARE) during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The XET C3VP dataset file includes temperature, pressure, wind speed and direction, relative humidity, solar radiation, grass temperature, soil temperature, snow depth, sunshine, and precipitation measurements from October 4, 2006 through March 31, 2007 in ASCII-csv format.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1998274501-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmxetc3vp__1/docs/RCSdataloggeroutputs_v2.003jun24.xls","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmxetc3vp__1/docs/gpmxetc3vp_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov/science/ground-validation/C3VP","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1998274501-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.ec.gc.ca/rs-mn/default.asp?lang=En&n=F382596F-1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.ocean-ops.org/dbcp/doc/AES_Canada_Guide20.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.5067/GPMGV/C3VP/METSTATION/DATA201","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-solar-radiation","earth-science-atmospheric-radiation-atmosphere-sunshine","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-cryospheric-indicators-climate-indicators-snow-depth","earth-science-precipitation-atmosphere-atmospheric-precipitation-indices","earth-science-soils-agriculture-soil-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -79.79, \"NorthBoundingCoordinate\": 44.24, \"EastBoundingCoordinate\": -79.77, \"SouthBoundingCoordinate\": 44.22}]]","temporal":"2006-10-04/2007-03-31","theme":["Earth Science"],"title":"GPM Ground Validation Environment Canada (EC) Weather Station XET C3VP V1"},"description":"The GPM Ground Validation Environment Canada (EC) Weather Station XET C3VP dataset consists of surface meteorological data collected at the Environment Canada (EC) XET station at the Centre for Atmospheric Research Experiments (CARE) during the Canadian CloudSat/CALIPSO Validation Project (C3VP) field campaign. The campaign took place in southern Canada in support of multiple science missions, including the NASA GPM mission, in order to improve the modeling and remote sensing of winter precipitation. The XET C3VP dataset file includes temperature, pressure, wind speed and direction, relative humidity, solar radiation, grass temperature, soil temperature, snow depth, sunshine, and precipitation measurements from October 4, 2006 through March 31, 2007 in ASCII-csv format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/460094dc-1110-41e2-9e98-aeb86792d509","harvest_record_raw":"https://catalog.data.gov/harvest_record/460094dc-1110-41e2-9e98-aeb86792d509/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPMGV/C3VP/METSTATION/DATA201","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-solar-radiation","earth-science-atmospheric-radiation-atmosphere-sunshine","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-cryospheric-indicators-climate-indicators-snow-depth","earth-science-precipitation-atmosphere-atmospheric-precipitation-indices","earth-science-soils-agriculture-soil-temperature"],"last_harvested_date":"2026-09-23T00:17:02.106360","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/MSFC/GHRC","slug":"gpm-ground-validation-environment-canada-ec-weather-station-xet-c3vp-v1","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM Ground Validation Environment Canada (EC) Weather Station XET C3VP V1","type":"dataset"},{"_score":12.94647,"_sort":[1790122621734,12.94647,1,"41c6b1aa-8e79-42db-999e-a39a34cd3687"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GPM Ground Validation Flight Summaries and Flight Tracks Imagery MC3E dataset for the Midlatitude Continental Convective Clouds Experiment (MC3E) provides processed summaries from the University of North Dakota including sonde maps, a radar animation, parameter versus time charts, radar track graphs, and a summary including aircraft and instrument operational times. The MC3E took place in central Oklahoma during the April-June 2011 period. The experiment was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The Flight Tracks imagery includes one animation for May 11, 2011 and the KICT 5 minute snapshots from the Real Time Mission Monitor (RTMM). This dataset is distributed with the MC3E ER-2 Navigation and the Citation Navigation datasets.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1979141270-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.5067/GPMGV/MC3E/DATA101","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmfltsummc3e__1/docs/gpmfltsummc3e_dataset.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/field-campaigns/mc3e","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1979141270-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPMGV/MC3E/FLIGHT/DATA201","keyword":["earth-science-platform-characteristics-spectral-engineering-flight-data-logs","earth-science-radar-spectral-engineering-radar-imagery"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -121, \"NorthBoundingCoordinate\": 43, \"EastBoundingCoordinate\": -91, \"SouthBoundingCoordinate\": 28}]]","temporal":"2011-04-25/2011-06-02","theme":["Earth Science"],"title":"GPM GROUND VALIDATION FLIGHT SUMMARIES AND FLIGHT TRACKS IMAGERY MC3E V1"},"description":"The GPM Ground Validation Flight Summaries and Flight Tracks Imagery MC3E dataset for the Midlatitude Continental Convective Clouds Experiment (MC3E) provides processed summaries from the University of North Dakota including sonde maps, a radar animation, parameter versus time charts, radar track graphs, and a summary including aircraft and instrument operational times. The MC3E took place in central Oklahoma during the April-June 2011 period. The experiment was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The Flight Tracks imagery includes one animation for May 11, 2011 and the KICT 5 minute snapshots from the Real Time Mission Monitor (RTMM). This dataset is distributed with the MC3E ER-2 Navigation and the Citation Navigation datasets.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ef4cd5a5-3195-447e-8374-e55669f67ba0","harvest_record_raw":"https://catalog.data.gov/harvest_record/ef4cd5a5-3195-447e-8374-e55669f67ba0/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPMGV/MC3E/FLIGHT/DATA201","keyword":["earth-science-platform-characteristics-spectral-engineering-flight-data-logs","earth-science-radar-spectral-engineering-radar-imagery"],"last_harvested_date":"2026-09-23T00:17:01.734740","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/MSFC/GHRC","slug":"gpm-ground-validation-flight-summaries-and-flight-tracks-imagery-mc3e-v1","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM GROUND VALIDATION FLIGHT SUMMARIES AND FLIGHT TRACKS IMAGERY MC3E V1","type":"dataset"},{"_score":8.942402,"_sort":[1790122614413,8.942402,1,"c93e6cb9-2406-4752-a565-a45027fbeafc"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GPM Ground Validation Mission Reports GCPEx dataset consists of various reports filed by the scientists during the GPM Cold-season Precipitation Experiment (GCPEx) campaign which took place from January 15 - February 29, 2012 in Ontario, Canada; however, this dataset spans from September 4, 2011 to May 11, 2012. GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinating model simulations of precipitating snow.  Report categories include the Mission Scientist, Mission Manager, Instrument Scientists, Weather Forecasts and Plan of Day. Many reports have additional information attached.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1979621014-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.5067/GPMGV/GCPEX/DATA101","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmmisrepgcpex__1/docs/GCPEX_CaseSummaryTable.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmmisrepgcpex__1/docs/gpmmisrepgcpex_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/field-campaigns/gcpex","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1979621014-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPMGV/GCPEX/REPORTS/DATA101","keyword":["earth-science-platform-characteristics-spectral-engineering-flight-data-logs","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-weather-events-atmosphere-weather-climate-advisories"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -80.7563, \"NorthBoundingCoordinate\": 46.9027, \"EastBoundingCoordinate\": -67.7583, \"SouthBoundingCoordinate\": 42.7243}]]","temporal":"2011-09-06/2012-05-11","theme":["Earth Science"],"title":"GPM GROUND VALIDATION MISSION REPORTS GCPEX V1"},"description":"The GPM Ground Validation Mission Reports GCPEx dataset consists of various reports filed by the scientists during the GPM Cold-season Precipitation Experiment (GCPEx) campaign which took place from January 15 - February 29, 2012 in Ontario, Canada; however, this dataset spans from September 4, 2011 to May 11, 2012. GCPEx addressed shortcomings in GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinating model simulations of precipitating snow.  Report categories include the Mission Scientist, Mission Manager, Instrument Scientists, Weather Forecasts and Plan of Day. Many reports have additional information attached.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8fcf359d-9806-497f-a3d6-0904a4a9c083","harvest_record_raw":"https://catalog.data.gov/harvest_record/8fcf359d-9806-497f-a3d6-0904a4a9c083/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPMGV/GCPEX/REPORTS/DATA101","keyword":["earth-science-platform-characteristics-spectral-engineering-flight-data-logs","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-weather-events-atmosphere-weather-climate-advisories"],"last_harvested_date":"2026-09-23T00:16:54.413242","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/MSFC/GHRC","slug":"gpm-ground-validation-mission-reports-gcpex-v1","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM GROUND VALIDATION MISSION REPORTS GCPEX V1","type":"dataset"},{"_score":12.123151,"_sort":[1790122603005,12.123151,1,"4724de40-9aaf-46d1-99f3-b907c63b7e83"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"ARCTAS focuses on advancing understanding of the factors driving current changes in the Arctic region including transport of mid-latitude pollution, boreal forest fires, aerosol radiative forcing, and chemical processes. ARCTAS aimed to use detailed observations from aircraft to provide the validation, retrieval constraints, correlative data, and process information needed to better achieve the potential of satellites for Arctic research. The plan is for the combination of satellite and aircraft data to provide together powerful information for constraining and evaluating models of Arctic atmospheric composition and climate, and thus improve model projections of future change. \n\nThe first phase of ARCTAS was based in Fairbanks and Barrow, Alaska with some flights to Thule, Greenland in April and focused on thick aerosol layers known as “arctic haze.” The second phase followed in July based from Cold Lake, Alberta and the Northwest Territories focusing on the emissions from large boreal forest fires in northwest Canada. \n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1617621679-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://acdisc.gesdisc.eosdis.nasa.gov/data/CAR/CAR_ARCTAS_BRDF.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://acdisc.gesdisc.eosdis.nasa.gov/opendap/hyrax/CAR/CAR_ARCTAS_BRDF.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://car.gsfc.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/CAR_ARCTAS_BRDF_2.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/CAR/CAR_BRDF_README-v2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/CAR_ARCTAS_L1C_1.jpg","format":"JPEG","mediaType":"image/jpeg"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1617621679-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SQSWDF366LDY","keyword":["earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-ocean-optics-oceans-reflectance","earth-science-surface-radiative-properties-land-surface-reflectance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -156.458, \"NorthBoundingCoordinate\": 80.6429, \"EastBoundingCoordinate\": -75.2635, \"SouthBoundingCoordinate\": 33.2106}]]","temporal":"2008-04-06/2008-07-10","theme":["Earth Science"],"title":"CAR ARCTAS BRDF Measurements  V2 (CAR_ARCTAS_BRDF)"},"description":"ARCTAS focuses on advancing understanding of the factors driving current changes in the Arctic region including transport of mid-latitude pollution, boreal forest fires, aerosol radiative forcing, and chemical processes. ARCTAS aimed to use detailed observations from aircraft to provide the validation, retrieval constraints, correlative data, and process information needed to better achieve the potential of satellites for Arctic research. The plan is for the combination of satellite and aircraft data to provide together powerful information for constraining and evaluating models of Arctic atmospheric composition and climate, and thus improve model projections of future change. \n\nThe first phase of ARCTAS was based in Fairbanks and Barrow, Alaska with some flights to Thule, Greenland in April and focused on thick aerosol layers known as “arctic haze.” The second phase followed in July based from Cold Lake, Alberta and the Northwest Territories focusing on the emissions from large boreal forest fires in northwest Canada. \n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/75ab80a8-c48d-4153-9134-b20d3a84c2c4","harvest_record_raw":"https://catalog.data.gov/harvest_record/75ab80a8-c48d-4153-9134-b20d3a84c2c4/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SQSWDF366LDY","keyword":["earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-ocean-optics-oceans-reflectance","earth-science-surface-radiative-properties-land-surface-reflectance"],"last_harvested_date":"2026-09-23T00:16:43.005071","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"car-arctas-brdf-measurements-v2-car_arctas_brdf-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CAR ARCTAS BRDF Measurements  V2 (CAR_ARCTAS_BRDF)","type":"dataset"},{"_score":18.546335,"_sort":[1790122601540,18.546335,1,"ea5720f9-0db2-4360-a96f-dd04ad01315f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This study promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1442068293-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://acdisc.gesdisc.eosdis.nasa.gov/data/CAR/CAR_ECO3D_L1C.1/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://acdisc.gesdisc.eosdis.nasa.gov/data/CAR/CAR_ECO3D_L1C.1/doc/CAR_README.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://acdisc.gsfc.nasa.gov/opendap/CAR/CAR_ECO3D_L1C.1/contents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://car.gsfc.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/CAR_ECO3D_L1C_1.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/CAR_ECO3D_L1C_1.jpg","format":"JPEG","mediaType":"image/jpeg"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1442068293-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CAR/DATA103","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-optics-oceans-reflectance","earth-science-surface-radiative-properties-land-surface-reflectance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -81.5599, \"NorthBoundingCoordinate\": 48.0604, \"EastBoundingCoordinate\": -68.3748, \"SouthBoundingCoordinate\": 24.7525}]]","temporal":"2011-08-19/2011-09-19","theme":["Earth Science"],"title":"CAR Eco-3D Vegetation Response to Changing Forcing Factors L1 V1 (CAR_ECO3D_L1C)"},"description":"This study promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/86775c5d-15e8-480b-ae80-7779ee74616d","harvest_record_raw":"https://catalog.data.gov/harvest_record/86775c5d-15e8-480b-ae80-7779ee74616d/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CAR/DATA103","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-optics-oceans-reflectance","earth-science-surface-radiative-properties-land-surface-reflectance"],"last_harvested_date":"2026-09-23T00:16:41.540924","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"car-eco-3d-vegetation-response-to-changing-forcing-factors-l1-v1-car_eco3d_l1c-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CAR Eco-3D Vegetation Response to Changing Forcing Factors L1 V1 (CAR_ECO3D_L1C)","type":"dataset"},{"_score":12.593344,"_sort":[1790122601163,12.593344,2,"8d30ac9a-586c-4e82-a207-d11dad4e9f6c"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAR Kuwait Oil Fire mission measured bidirectional reflectance function of smoke from Kuwait oil fires during the Kuwait Oil Fire Smoke Experiment. Measurements were also taken over the Saudi Arabian desert with overlying desert dust, and Persian Gulf waters with some overlying aerosols. This experiment was a part of an international research effort in response to an environmental crisis, when over 600 oil wells in Kuwait were ignited by Iraqi forces in 1991. The resulting fires produced large plumes of smoke that had significant effects on the Persian Gulf region but limited global effects. Between May 16 and June 12, 1991, the Kuwait Oil Fire Smoke Experiment (KOFSE) was conducted in the Persian Gulf Region. The purpose of KOFSE was to determine the chemical and physical nature of the smoke and to investigate its potential effects on air quality, weather, and climate.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1617621689-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://acdisc.gesdisc.eosdis.nasa.gov/data/CAR/CAR_KUWAITOILFIRE_BRDF.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://acdisc.gsfc.nasa.gov/opendap/CAR/CAR_KUWAITOILFIRE_BRDF.2/contents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://car.gsfc.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/CAR_KUWAITOILFIRE_BRDF_2.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/CAR/CAR_BRDF_README-v2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/CAR_KUWAITOILFIRE_L1C_1.jpg","format":"JPEG","mediaType":"image/jpeg"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1617621689-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/TLTYU661EXN6","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-optics-oceans-reflectance","earth-science-surface-radiative-properties-land-surface-reflectance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": 46.9617, \"NorthBoundingCoordinate\": 28.602, \"EastBoundingCoordinate\": 49.3732, \"SouthBoundingCoordinate\": 27.8542}]]","temporal":"1991-05-18/1991-06-02","theme":["Earth Science"],"title":"CAR Kuwait Oil Fire BRDF V2 (CAR_KUWAITOILFIRE_BRDF)"},"description":"CAR Kuwait Oil Fire mission measured bidirectional reflectance function of smoke from Kuwait oil fires during the Kuwait Oil Fire Smoke Experiment. Measurements were also taken over the Saudi Arabian desert with overlying desert dust, and Persian Gulf waters with some overlying aerosols. This experiment was a part of an international research effort in response to an environmental crisis, when over 600 oil wells in Kuwait were ignited by Iraqi forces in 1991. The resulting fires produced large plumes of smoke that had significant effects on the Persian Gulf region but limited global effects. Between May 16 and June 12, 1991, the Kuwait Oil Fire Smoke Experiment (KOFSE) was conducted in the Persian Gulf Region. The purpose of KOFSE was to determine the chemical and physical nature of the smoke and to investigate its potential effects on air quality, weather, and climate.\n\nThis data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2113f66b-f8be-42f3-b673-2cde604d4522","harvest_record_raw":"https://catalog.data.gov/harvest_record/2113f66b-f8be-42f3-b673-2cde604d4522/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/TLTYU661EXN6","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-optics-oceans-reflectance","earth-science-surface-radiative-properties-land-surface-reflectance"],"last_harvested_date":"2026-09-23T00:16:41.163525","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"car-kuwait-oil-fire-brdf-v2-car_kuwaitoilfire_brdf-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CAR Kuwait Oil Fire BRDF V2 (CAR_KUWAITOILFIRE_BRDF)","type":"dataset"},{"_score":10.068407,"_sort":[1790122589434,10.068407,4,"0935f711-22ca-4558-a4d4-4db6a9ee5fd1"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS).  The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E).  The FLDAS regional monthly datasets will no longer be available and have been superseded by the global monthly dataset. \n\nThe simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit.\n\nThe simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.\n\nIn November 2020, all FLDAS data were\u00a0post-processed with the MOD44W MODIS land mask. \u00a0Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.\u00a0 The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect\u00a0the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site.  If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1563089663-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1563089663-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/FLDAS_NOAH01_C_GL_M_001.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%2520Documentation","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/howto?tags=hydrology","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/FLDAS_NOAH01_C_GL_M_001.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/hydrology/README_FLDAS.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://giovanni.gsfc.nasa.gov/giovanni/#dataKeyword=FLDAS_NOAH01_C_GL_M","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://hydro1.gesdisc.eosdis.nasa.gov/data/FLDAS/FLDAS_NOAH01_C_GL_M.001/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ldas.gsfc.nasa.gov/FLDAS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ldas.gsfc.nasa.gov/fldas/vegetation-class","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1563089663-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/5NHC22T9375G","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-snow-depth","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -60}]]","temporal":"1982-01-01/2026-09-14","theme":["Earth Science"],"title":"FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M)"},"description":"This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS).  The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E).  The FLDAS regional monthly datasets will no longer be available and have been superseded by the global monthly dataset. \n\nThe simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit.\n\nThe simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.\n\nIn November 2020, all FLDAS data were\u00a0post-processed with the MOD44W MODIS land mask. \u00a0Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.\u00a0 The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect\u00a0the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site.  If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2eed7fe9-ccce-447b-ad63-bd6c52d0f221","harvest_record_raw":"https://catalog.data.gov/harvest_record/2eed7fe9-ccce-447b-ad63-bd6c52d0f221/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/5NHC22T9375G","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-snow-depth","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-23T00:16:29.434891","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":4,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"fldas-noah-land-surface-model-l4-global-monthly-0-1-x-0-1-degree-merra-2-and-chirps-v001-f","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M)","type":"dataset"},{"_score":8.230154,"_sort":[1790122587318,8.230154,3,"68ed2eb7-7b42-4518-9be1-e424fa3daacc"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2.  GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.  GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present.  GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation).  The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.2 is new to the GES DISC archive and currently includes a main product from CLSM-F2.5 with Data Assimilation for the Gravity Recovery and Climate Experiment (GRACE-DA) from February 2003 to present.  The GLDAS-2.2 data are available in two production streams: one with GRACE data assimilation outputs (the main production stream), and one without GRACE-DA (the early production stream).  Since the GRACE data have a 2-6 month latency, the GLDAS-2.2 data are first created without GRACE-DA, and are designated as the Early Product (EP), with about 1 month latency.  Once the GRACE data become available, the GLDAS-2.2 data are processed with GRACE-DA in the main production stream and are removed from the Early Product archive. \n\nThis data product is an Early Product for GLDAS-2.2 Catchment Land Surface Model daily 0.25 x 0.25 degree with GRACE-DA. \n\nThe GLDAS-2.2 GARCE-DA product was simulated with Catchment-F2.5 in Land Information System (LIS) Version 7. The data product contains 24 land surface fields from February 1, 2003 to present.\n\nThe simulation started on February 1, 2003 using the conditions from the GLDAS-2.0 Daily Catchment model simulation, forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System.  The total terrestrial water anomaly observation from GRACE satellite was assimilated (Li et al, 2019). Due to the data agreement with ECMWF, this GLDAS-2.2 daily product does not include the meteorological forcing fields.\n\nThe GLDAS-2.2 data are archived and distributed in NetCDF format.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1700900822-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GLDAS_CLSM025_DA1_D_EP_2.2.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%2520Documentation","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/howto?tags=hydrology","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GLDAS_CLSM025_DA1_D_EP_2.2.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/hydrology/README_GLDAS2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_CLM10SUBP_3H_Status_and_Related_Data_Collections.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_LSM_Description.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_CLSM025_DA1_D_EP.2.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ldas.gsfc.nasa.gov/gldas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1700900822-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/IIU5JWU2AGRP","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -60}]]","temporal":"2020-01-01/2026-09-14","theme":["Earth Science"],"title":"GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree GRACE-DA1 V2.2 (GLDAS_CLSM025_DA1_D_EP)"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2.  GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.  GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present.  GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation).  The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nGLDAS-2.2 is new to the GES DISC archive and currently includes a main product from CLSM-F2.5 with Data Assimilation for the Gravity Recovery and Climate Experiment (GRACE-DA) from February 2003 to present.  The GLDAS-2.2 data are available in two production streams: one with GRACE data assimilation outputs (the main production stream), and one without GRACE-DA (the early production stream).  Since the GRACE data have a 2-6 month latency, the GLDAS-2.2 data are first created without GRACE-DA, and are designated as the Early Product (EP), with about 1 month latency.  Once the GRACE data become available, the GLDAS-2.2 data are processed with GRACE-DA in the main production stream and are removed from the Early Product archive. \n\nThis data product is an Early Product for GLDAS-2.2 Catchment Land Surface Model daily 0.25 x 0.25 degree with GRACE-DA. \n\nThe GLDAS-2.2 GARCE-DA product was simulated with Catchment-F2.5 in Land Information System (LIS) Version 7. The data product contains 24 land surface fields from February 1, 2003 to present.\n\nThe simulation started on February 1, 2003 using the conditions from the GLDAS-2.0 Daily Catchment model simulation, forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System.  The total terrestrial water anomaly observation from GRACE satellite was assimilated (Li et al, 2019). Due to the data agreement with ECMWF, this GLDAS-2.2 daily product does not include the meteorological forcing fields.\n\nThe GLDAS-2.2 data are archived and distributed in NetCDF format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/afa1f7df-ec7c-4477-8465-0ea47aadf7db","harvest_record_raw":"https://catalog.data.gov/harvest_record/afa1f7df-ec7c-4477-8465-0ea47aadf7db/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/IIU5JWU2AGRP","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-23T00:16:27.318591","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gldas-catchment-land-surface-model-l4-daily-0-25-x-0-25-degree-grace-da1-v2-2-gldas_clsm02","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree GRACE-DA1 V2.2 (GLDAS_CLSM025_DA1_D_EP)","type":"dataset"},{"_score":9.63513,"_sort":[1790122586947,9.63513,6,"3cb35431-43cb-4d1a-9bb6-115266b7e865"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 0.25 degree daily, contains a series of land surface parameters simulated from the Catchment Land Surface Model 3.6, and currently covers from January 1948 to December 2014.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO’s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products. \n\nThe GLDAS-2.0 data are archived and distributed in netCDF format.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1453188197-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1453188197-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GLDAS_CLSM025_D_2.0.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%2520Documentation","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/howto?tags=hydrology","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GLDAS_CLSM025_D_2.0.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/hydrology/README_GLDAS2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_CLM10SUBP_3H_Status_and_Related_Data_Collections.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_LSM_Description.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://giovanni.gsfc.nasa.gov/giovanni/#dataKeyword=GLDAS_CLSM025_D_2_0","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_CLSM025_D.2.0/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ldas.gsfc.nasa.gov/gldas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1453188197-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/LYHA9088MFWQ","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -60}]]","temporal":"1948-01-01/2014-12-30","theme":["Earth Science"],"title":"GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree V2.0 (GLDAS_CLSM025_D)"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 0.25 degree daily, contains a series of land surface parameters simulated from the Catchment Land Surface Model 3.6, and currently covers from January 1948 to December 2014.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO’s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products. \n\nThe GLDAS-2.0 data are archived and distributed in netCDF format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7ef9111e-c7f2-4f38-9270-afbfe1c1087b","harvest_record_raw":"https://catalog.data.gov/harvest_record/7ef9111e-c7f2-4f38-9270-afbfe1c1087b/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/LYHA9088MFWQ","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-23T00:16:26.947761","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":6,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gldas-catchment-land-surface-model-l4-daily-0-25-x-0-25-degree-v2-0-gldas_clsm025_d-at-ges","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree V2.0 (GLDAS_CLSM025_D)","type":"dataset"},{"_score":7.419388,"_sort":[1790122586584,7.419388,1,"417510ac-7a61-4e2f-abf9-700ca8c413a2"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2.  GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.  GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present.  GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation).  The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 monthly 1.0 degree, contains a series of land surface variables generated through temporal averaging of GLDAS-2.0 3-hourly data simulated from the Catchment Land Surface Model 3.6 in Land Information System (LIS) Version 7.  The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask.  Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.  The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README.  The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1933574465-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1933574465-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GLDAS_CLSM10_M_2.0.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%2520Documentation","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/howto?tags=hydrology","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GLDAS_CLSM10_M_2.0.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/hydrology/README_GLDAS2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_CLM10SUBP_3H_Status_and_Related_Data_Collections.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_LSM_Description.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_CLSM10_M.2.0/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ldas.gsfc.nasa.gov/gldas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1933574465-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SGSL3LNKGJWW","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -60}]]","temporal":"1948-01-01/2014-12-31","theme":["Earth Science"],"title":"GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree V2.0 (GLDAS_CLSM10_M)"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2.  GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.  GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present.  GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation).  The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 monthly 1.0 degree, contains a series of land surface variables generated through temporal averaging of GLDAS-2.0 3-hourly data simulated from the Catchment Land Surface Model 3.6 in Land Information System (LIS) Version 7.  The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask.  Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.  The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README.  The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8ad59a51-c8c6-40a0-9d8a-35b802ef19ea","harvest_record_raw":"https://catalog.data.gov/harvest_record/8ad59a51-c8c6-40a0-9d8a-35b802ef19ea/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SGSL3LNKGJWW","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-23T00:16:26.584640","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gldas-catchment-land-surface-model-l4-monthly-1-0-x-1-0-degree-v2-0-gldas_clsm10_m-at-ges-","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree V2.0 (GLDAS_CLSM10_M)","type":"dataset"},{"_score":7.94812,"_sort":[1790122581208,7.94812,1,"d9edb300-954c-4f47-a130-0874f929ec88"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2.  GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.  GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present.  GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation).  The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 VIC 3-hourly 1.0 degree, contains a series of land surface variables simulated simulated with the Variable Infiltration Capacity (VIC) 4.1.2 Land Surface Model in Land Information System (LIS) Version 7.  The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask.  Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.  The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README.  The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1933574580-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1933574580-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GLDAS_VIC10_3H_2.0.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%2520Documentation","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/howto?tags=hydrology","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GLDAS_VIC10_3H_2.0.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/hydrology/README_GLDAS2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_CLM10SUBP_3H_Status_and_Related_Data_Collections.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_LSM_Description.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_VIC10_3H.2.0/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ldas.gsfc.nasa.gov/gldas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1933574580-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/B6OMXPRI022J","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -60}]]","temporal":"1948-01-01/2014-12-31","theme":["Earth Science"],"title":"GLDAS VIC Land Surface Model L4 3 hourly 1.0 x 1.0 degree V2.0 (GLDAS_VIC10_3H)"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2.  GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.  GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present.  GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation).  The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set, GLDAS-2.0 VIC 3-hourly 1.0 degree, contains a series of land surface variables simulated simulated with the Variable Infiltration Capacity (VIC) 4.1.2 Land Surface Model in Land Information System (LIS) Version 7.  The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask.  Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.  The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README.  The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6e03a244-4fa1-4085-942c-e436aac0e15b","harvest_record_raw":"https://catalog.data.gov/harvest_record/6e03a244-4fa1-4085-942c-e436aac0e15b/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/B6OMXPRI022J","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-23T00:16:21.208750","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gldas-vic-land-surface-model-l4-3-hourly-1-0-x-1-0-degree-v2-0-gldas_vic10_3h-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GLDAS VIC Land Surface Model L4 3 hourly 1.0 x 1.0 degree V2.0 (GLDAS_VIC10_3H)","type":"dataset"},{"_score":8.464098,"_sort":[1790122570561,8.464098,1,"7589a56b-0cfe-4089-9226-5ac1dc0eff89"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRX2SUP contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML2CCPRETN contains CRIMSS data processed with an analogous algorithm.  A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633993908-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/SNDRSNIML2CHTRETN_1.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/SNDRSNIML2CHTRETN_01.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART.CLIMCAPS.v1.L2.README.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART_V1.ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633993908-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/data/limited_editions/SNPP_CHART/SNDRSNIML2CHTRETN.1/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/opendap/limited_editions/SNPP_CHART/SNDRSNIML2CHTRETN.1/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/6U9XTLE2XOTA","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-altitude-atmosphere-tropopause","earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-outgoing-longwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-temperature-oceans-sea-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-skin-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2013-01-01/2015-11-01","theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 2 CHART Normal Spectral Resolution: Atmosphere, cloud and surface geophysical state V1"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRX2SUP contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML2CCPRETN contains CRIMSS data processed with an analogous algorithm.  A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/dcc4b628-4383-4779-9686-a40c8ad4efde","harvest_record_raw":"https://catalog.data.gov/harvest_record/dcc4b628-4383-4779-9686-a40c8ad4efde/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/6U9XTLE2XOTA","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-altitude-atmosphere-tropopause","earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-outgoing-longwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-temperature-oceans-sea-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-skin-temperature"],"last_harvested_date":"2026-09-23T00:16:10.561072","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"sounder-sips-suomi-npp-crimss-level-2-chart-normal-spectral-resolution-atmosphere-cloud-an","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 2 CHART Normal Spectral Resolution: Atmosphere, cloud and surface geophysical state V1","type":"dataset"},{"_score":7.8702393,"_sort":[1790122570205,7.8702393,3,"9916a5cf-b5ce-45e3-b88d-308b48174ef0"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. \n\nCloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRI2CCR contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML2CCPCCRN contains CRIMSS data processed with an analogous algorithm. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633993911-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/SNDRSNIML2CHTCCRN_1.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/SNDRSNIML2CHTCCRN_01.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART.CLIMCAPS.v1.L2.README.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART_V1.ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633993911-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/data/limited_editions/SNPP_CHART/SNDRSNIML2CHTCCRN.1/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/opendap/limited_editions/SNPP_CHART/SNDRSNIML2CHTCCRN.1/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/9EJ3W4OBG0E6","keyword":["earth-science-infrared-wavelengths-spectral-engineering-brightness-temperature","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-microwave-spectral-engineering-brightness-temperature","earth-science-microwave-spectral-engineering-microwave-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2013-01-01/2015-11-01","theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 2 CHART Normal Spectral Resolution: Cloud Cleared Radiances V1"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. \n\nCloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRI2CCR contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML2CCPCCRN contains CRIMSS data processed with an analogous algorithm. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2cab3500-d03e-4381-bdd1-767fa345ff4f","harvest_record_raw":"https://catalog.data.gov/harvest_record/2cab3500-d03e-4381-bdd1-767fa345ff4f/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/9EJ3W4OBG0E6","keyword":["earth-science-infrared-wavelengths-spectral-engineering-brightness-temperature","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-microwave-spectral-engineering-brightness-temperature","earth-science-microwave-spectral-engineering-microwave-radiance"],"last_harvested_date":"2026-09-23T00:16:10.205958","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"sounder-sips-suomi-npp-crimss-level-2-chart-normal-spectral-resolution-cloud-cleared-radia","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 2 CHART Normal Spectral Resolution: Cloud Cleared Radiances V1","type":"dataset"},{"_score":8.229258,"_sort":[1790122569133,8.229258,3,"015f60de-1b45-47e3-bf37-02b2a33bbc20"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables. \n \nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection with the TqJ suffix (TqJoint) from AIRX3SPD contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML3CDCCPN contains CRIMSS data processed with an analogous algorithm.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633993917-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/SNDRSNIML3CDCHTN_1.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/SNDRSNIML3CDCHTN_01.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART.CLIMCAPS.v1.L3.README.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART_V1.ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633993917-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/data/limited_editions/SNPP_CHART/SNDRSNIML3CDCHTN.1/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/opendap/limited_editions/SNPP_CHART/SNDRSNIML3CDCHTN.1/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/HSYAN76MWPGR","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-altitude-atmosphere-tropopause","earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-outgoing-longwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-temperature-oceans-sea-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-skin-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2013-01-01/2015-11-01","theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 3 Comprehensive Quality Control Gridded Daily CHART Normal Spectral Resolution V1"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the comprehensive quality control (QC) methodology. Comprehensive QC accepts a retrieval if the profile is good to the surface and ensures consistent analysis across all levels and variables. \n \nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection with the TqJ suffix (TqJoint) from AIRX3SPD contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML3CDCCPN contains CRIMSS data processed with an analogous algorithm.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d7f59d8a-a6d3-4184-9587-85ed567b0f83","harvest_record_raw":"https://catalog.data.gov/harvest_record/d7f59d8a-a6d3-4184-9587-85ed567b0f83/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/HSYAN76MWPGR","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-altitude-atmosphere-tropopause","earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-outgoing-longwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-temperature-oceans-sea-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-skin-temperature"],"last_harvested_date":"2026-09-23T00:16:09.133809","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"sounder-sips-suomi-npp-crimss-level-3-comprehensive-quality-control-gridded-daily-chart-no","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 3 Comprehensive Quality Control Gridded Daily CHART Normal Spectral Resolution V1","type":"dataset"},{"_score":7.94812,"_sort":[1790122568410,7.94812,2,"451a8ade-3ce4-436e-b762-dfad738ede3d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying profile level specific quality control (QC). Specific QC is defined per retrieved geophysical parameter at a given level within a profile. It accepts profile level data from the top of the atmosphere down to the level where the QC algorithm determines that the retrieval is good. Below this level, the data is rejected. Specific QC is designed to maximize the yield of each variable. This is the same methodology used by the AIRS Version 6 processing system.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRX3SPD contains similar meteorological information to this CHART data collection.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633993922-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/SNDRSNIML3SDCHTN_1.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/SNDRSNIML3SDCHTN_01.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART.CLIMCAPS.v1.L3.README.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART_V1.ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633993922-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/data/limited_editions/SNPP_CHART/SNDRSNIML3SDCHTN.1/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sounder.gesdisc.eosdis.nasa.gov/opendap/limited_editions/SNPP_CHART/SNDRSNIML3SDCHTN.1/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/TFW6B4G48YNL","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-altitude-atmosphere-tropopause","earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-outgoing-longwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-temperature-oceans-sea-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-skin-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2013-01-01/2015-11-01","theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 3 Specific Quality Control Gridded Daily CHART Normal Spectral Resolution V1"},"description":"The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite).  The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\n\nThis daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying profile level specific quality control (QC). Specific QC is defined per retrieved geophysical parameter at a given level within a profile. It accepts profile level data from the top of the atmosphere down to the level where the QC algorithm determines that the retrieval is good. Below this level, the data is rejected. Specific QC is designed to maximize the yield of each variable. This is the same methodology used by the AIRS Version 6 processing system.\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRX3SPD contains similar meteorological information to this CHART data collection.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/fc941263-3953-43ff-b035-13eeebdab15a","harvest_record_raw":"https://catalog.data.gov/harvest_record/fc941263-3953-43ff-b035-13eeebdab15a/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/TFW6B4G48YNL","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-altitude-atmosphere-tropopause","earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-outgoing-longwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-properties","earth-science-ocean-temperature-oceans-sea-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-skin-temperature"],"last_harvested_date":"2026-09-23T00:16:08.410279","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"sounder-sips-suomi-npp-crimss-level-3-specific-quality-control-gridded-daily-chart-normal-","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Sounder SIPS: Suomi NPP CrIMSS Level 3 Specific Quality Control Gridded Daily CHART Normal Spectral Resolution V1","type":"dataset"},{"_score":9.63513,"_sort":[1790122552114,9.63513,1,"e094102d-0e95-4435-bbee-186c5df8d16e"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The SWOT Level 2 Lake Single-Pass Vector Product (SWOT_L2_HR_LakeSP_D) provides geolocated surface water measurements for lakes and unclassified water bodies, derived from high-resolution radar observations collected by the Ka-band Radar Interferometer (KaRIn) on the SWOT satellite. This product reports water surface elevation, surface area, and quality indicators for each observed lake or feature, with polygons generated from the PIXCVec pixel cloud. It includes features linked to the Prior Lake Database (PLD), as well as unassigned features not recognized as lakes or rivers in the PLD or PRD.\n\nEach granule covers a full KaRIn swath for a single continental pass and contains three ESRI shapefiles: one observation-oriented file of observed PLD lakes, one PLD-oriented file with prior lake features (including unobserved ones), and one file for unassigned features. Observed quantities are referenced to the WGS84 ellipsoid and corrected for geoid height, solid Earth, load, and pole tides, as well as tropospheric and ionospheric path delays. Attributes also include uncertainties, flags for partial or dark water coverage, ice cover, and crossover calibration. Storage change estimates are reported in the PLD-oriented shapefile using both direct and incremental methods under different bathymetric assumptions.\n\nThe LakeSP product provides consistent reach-scale and basin-level lake measurements suitable for time series analyses, hydrologic modeling, and integration with prior lake databases. It enables monitoring of water level changes and surface area dynamics across a wide range of inland water bodies and supports hydrological applications in water balance studies, reservoir management, and climate impact assessment.\n\nThis dataset is the parent collection to the following sub-collections: \n\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_obs_D \n\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_prior_D \n\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_unassigned_D","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3233944983-POCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swot_mission_docs/D-109532_SWOT_UserHandbook_RevA_20250311_sig-final.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swot_mission_docs/SWOT_VersionD_KaRIn_Products_Release_Note_20250423b.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swot_mission_docs/atbd/SWOT-NT-CDM-1753-CNES_ATBD_LakeSP_20230726_Initial_w-sigs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swot_mission_docs/pdd/SWOT-TN-CDM-0673-CNES_Product_Description_L2_HR_LakeSP_20250307_RevC_signed.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3233944983-POCLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://github.com/podaac/data-subscriber","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/CitingPODAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/Podaac/thumbnails/SWOT_L2_HR_LakeSP_D.jpg","format":"JPEG","mediaType":"image/jpeg"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/swot","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3233944983-POCLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://swot.jpl.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.aviso.altimetry.fr/en/missions/current-missions/swot.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://www.jpl.nasa.gov/missions/surface-water-and-ocean-topography-swot","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.nasa.gov/swot","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SWOT-LAKESP-D","keyword":["earth-science-surface-water-terrestrial-hydrosphere-surface-water-features","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Jet Propulsion Laboratory;NASA/JPL/PODAAC"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","theme":["Earth Science"],"title":"SWOT Level 2 Lake Single-Pass Vector Data Product, Version D"},"description":"The SWOT Level 2 Lake Single-Pass Vector Product (SWOT_L2_HR_LakeSP_D) provides geolocated surface water measurements for lakes and unclassified water bodies, derived from high-resolution radar observations collected by the Ka-band Radar Interferometer (KaRIn) on the SWOT satellite. This product reports water surface elevation, surface area, and quality indicators for each observed lake or feature, with polygons generated from the PIXCVec pixel cloud. It includes features linked to the Prior Lake Database (PLD), as well as unassigned features not recognized as lakes or rivers in the PLD or PRD.\n\nEach granule covers a full KaRIn swath for a single continental pass and contains three ESRI shapefiles: one observation-oriented file of observed PLD lakes, one PLD-oriented file with prior lake features (including unobserved ones), and one file for unassigned features. Observed quantities are referenced to the WGS84 ellipsoid and corrected for geoid height, solid Earth, load, and pole tides, as well as tropospheric and ionospheric path delays. Attributes also include uncertainties, flags for partial or dark water coverage, ice cover, and crossover calibration. Storage change estimates are reported in the PLD-oriented shapefile using both direct and incremental methods under different bathymetric assumptions.\n\nThe LakeSP product provides consistent reach-scale and basin-level lake measurements suitable for time series analyses, hydrologic modeling, and integration with prior lake databases. It enables monitoring of water level changes and surface area dynamics across a wide range of inland water bodies and supports hydrological applications in water balance studies, reservoir management, and climate impact assessment.\n\nThis dataset is the parent collection to the following sub-collections: \n\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_obs_D \n\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_prior_D \n\nhttps://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_unassigned_D","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c3f1fb31-d165-4b79-b891-fd29cba760bb","harvest_record_raw":"https://catalog.data.gov/harvest_record/c3f1fb31-d165-4b79-b891-fd29cba760bb/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SWOT-LAKESP-D","keyword":["earth-science-surface-water-terrestrial-hydrosphere-surface-water-features","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-23T00:15:52.114441","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"Jet Propulsion Laboratory;NASA/JPL/PODAAC","slug":"swot-level-2-lake-single-pass-vector-data-product-version-d","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"SWOT Level 2 Lake Single-Pass Vector Data Product, Version D","type":"dataset"},{"_score":26.73764,"_sort":[1790122531990,26.73764,3,"2d4edf1f-9338-4994-95c9-9c1aad995ab6"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3550185878-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550185878-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/ndh-drought-hazard-frequency-distribution/maps/services","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.7927/H4VX0DFT","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-droughts","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]","temporal":"1980-01-01/2000-12-31","theme":["Earth Science"],"title":"Global Drought Hazard Frequency and Distribution"},"description":"The Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/637a9045-8127-499f-9e11-72c76c19cae9","harvest_record_raw":"https://catalog.data.gov/harvest_record/637a9045-8127-499f-9e11-72c76c19cae9/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4VX0DFT","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-droughts","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"last_harvested_date":"2026-09-23T00:15:31.990084","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"ESDIS","slug":"global-drought-hazard-frequency-and-distribution-4fad4","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Global Drought Hazard Frequency and Distribution","type":"dataset"},{"_score":10.457558,"_sort":[1790122531635,10.457558,4,"0e63b564-a902-478d-836e-d2b5c12e92ce"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of drought-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3550188048-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550188048-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/ndh-drought-mortality-risks-distribution/maps/services","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.7927/H4R49NQV","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-droughts","earth-science-population-human-dimensions-mortality","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]","temporal":"2000-01-01/2000-12-31","theme":["Earth Science"],"title":"Global Drought Mortality Risks and Distribution"},"description":"The Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of drought-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/36d92fa1-ae8c-438d-ba3b-075e7f6ba7d5","harvest_record_raw":"https://catalog.data.gov/harvest_record/36d92fa1-ae8c-438d-ba3b-075e7f6ba7d5/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4R49NQV","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-droughts","earth-science-population-human-dimensions-mortality","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"last_harvested_date":"2026-09-23T00:15:31.635426","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":4,"publisher":"ESDIS","slug":"global-drought-mortality-risks-and-distribution-dc4bc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Global Drought Mortality Risks and Distribution","type":"dataset"},{"_score":18.922836,"_sort":[1790122527376,18.922836,1,"5ddaf510-e103-4514-8b0a-da930656ad1f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3540909397-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3540909397-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/about/esdis","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.7927/nj0x-8y67","keyword":["earth-science-land-use-land-cover-land-surface"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-01-01/2100-12-31","theme":["Earth Science"],"title":"Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100"},"description":"The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0bdad0b2-f2c5-4b8e-bc55-f186022876a0","harvest_record_raw":"https://catalog.data.gov/harvest_record/0bdad0b2-f2c5-4b8e-bc55-f186022876a0/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/nj0x-8y67","keyword":["earth-science-land-use-land-cover-land-surface"],"last_harvested_date":"2026-09-23T00:15:27.376513","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ESDIS","slug":"global-one-eighth-degree-urban-land-extent-projection-and-base-year-grids-by-ssp-2000-2100-846a6","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100","type":"dataset"},{"_score":10.8109455,"_sort":[1790122525593,10.8109455,5,"d25fd9d6-8bc5-427c-90d2-963b6107ed88"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3550186069-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550186069-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution/maps/services","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.7927/H4668B3D","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-floods","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]","temporal":"1985-01-01/2003-12-31","theme":["Earth Science"],"title":"Global Flood Hazard Frequency and Distribution"},"description":"The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/44fc8ec6-b715-4016-aef0-8c6de862877d","harvest_record_raw":"https://catalog.data.gov/harvest_record/44fc8ec6-b715-4016-aef0-8c6de862877d/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4668B3D","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-floods","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"last_harvested_date":"2026-09-23T00:15:25.593280","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":5,"publisher":"ESDIS","slug":"global-flood-hazard-frequency-and-distribution-a536d","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Global Flood Hazard Frequency and Distribution","type":"dataset"},{"_score":10.068407,"_sort":[1790122525223,10.068407,1,"1ee18010-0cbd-4418-a71b-c114157b9cbd"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 3.3 is the current version. Older versions have been superseded by Version 3.3.\n\nProduct latency/update: The products are currently paused at September 2024 because the IR input dataset from NCEI requires a new calibration scheme to extend past that point. Once NCEI irons out the calibration, we expect to return to quarterly updates.\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Experiment (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program.  As the follow on to the GPCP Version 2.X products, GPCP Version 3 (GPCP V3.3) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets.  The GPCPV3 suite currently consists the 0.5-degree monthly and daily products.  A follow-on 0.1-degree 3-hourly is expected.  All GPCPV3 products will be internally consistent. Inputs consist of the GPROF SSMI/SSMIS orbit files that are used to calibrate the PERSIANN-CDR IR-based precipitation in the region 58\u00b0N-S, which are in turn adjusted to the monthly climatological MCTG.  Outside of 58\u00b0N-S, TOVS/AIRS estimates, adjusted climatologically to the MCTG, are used.  The PERSIANN-CDR/TOVS/AIRS estimates are then merged in the region 35\u00b0N-S-58\u00b0N-S, which are then merged with GPCC gauge analyses over land to obtain the final product. In addition to the final precipitation field, ancillary precipitation and error estimates are provided.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3405935516-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPCPMON_3.3.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GPCPMON_V33.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/GPCP_ATBD_V3.3_Monthly.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/Release_Notes.GPCPV3.3.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://giovanni.gsfc.nasa.gov/giovanni/#dataKeyword=GPCP%20monthly%20V3.3","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/GPCP/GPCPMON.3.3/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/GPCP/GPCPMON.3.3/doc/README.GPCPV3.3_Monthly.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/opendap/GPCP/GPCPMON.3.3/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3405935516-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MEASURES/GPCP/DATA306","keyword":["earth-science-precipitation-atmosphere","earth-science-precipitation-atmosphere-precipitation-rate"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1983-01-01/2025-12-31","theme":["Earth Science"],"title":"GPCP Precipitation Level 3 Monthly 0.5-Degree V3.3 (GPCPMON)"},"description":"Version 3.3 is the current version. Older versions have been superseded by Version 3.3.\n\nProduct latency/update: The products are currently paused at September 2024 because the IR input dataset from NCEI requires a new calibration scheme to extend past that point. Once NCEI irons out the calibration, we expect to return to quarterly updates.\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Experiment (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program.  As the follow on to the GPCP Version 2.X products, GPCP Version 3 (GPCP V3.3) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets.  The GPCPV3 suite currently consists the 0.5-degree monthly and daily products.  A follow-on 0.1-degree 3-hourly is expected.  All GPCPV3 products will be internally consistent. Inputs consist of the GPROF SSMI/SSMIS orbit files that are used to calibrate the PERSIANN-CDR IR-based precipitation in the region 58\u00b0N-S, which are in turn adjusted to the monthly climatological MCTG.  Outside of 58\u00b0N-S, TOVS/AIRS estimates, adjusted climatologically to the MCTG, are used.  The PERSIANN-CDR/TOVS/AIRS estimates are then merged in the region 35\u00b0N-S-58\u00b0N-S, which are then merged with GPCC gauge analyses over land to obtain the final product. In addition to the final precipitation field, ancillary precipitation and error estimates are provided.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/332709d6-5409-436f-bb2c-9373a7d939af","harvest_record_raw":"https://catalog.data.gov/harvest_record/332709d6-5409-436f-bb2c-9373a7d939af/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MEASURES/GPCP/DATA306","keyword":["earth-science-precipitation-atmosphere","earth-science-precipitation-atmosphere-precipitation-rate"],"last_harvested_date":"2026-09-23T00:15:25.223689","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpcp-precipitation-level-3-monthly-0-5-degree-v3-3-gpcpmon-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPCP Precipitation Level 3 Monthly 0.5-Degree V3.3 (GPCPMON)","type":"dataset"},{"_score":13.338382,"_sort":[1790122524493,13.338382,1,"ed7d5b66-611e-4b9f-ba2d-6b244430d812"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 5 x 10 km (cross-track x along-track at forward bore sight).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954781-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954781-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954781-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFGCOMW1AMSR2_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFGCOMW1AMSR2_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954781-GES_DISC&q=GPM_2AGPROFGCOMW1AMSR2_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://suzaku.eorc.jaxa.jp/GCOM_W/w_amsr2/amsr2_body_main.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/amsr2.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/AMSR2/GCOMW1/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2014-01-31/2026-09-14","theme":["Earth Science"],"title":"GPM AMSR-2 on GCOM-W1 (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 10 km V08 (GPM_2AGPROFGCOMW1AMSR2)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 5 x 10 km (cross-track x along-track at forward bore sight).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/93a9c15f-3e70-4b09-83f9-fdf4ed528fbe","harvest_record_raw":"https://catalog.data.gov/harvest_record/93a9c15f-3e70-4b09-83f9-fdf4ed528fbe/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/AMSR2/GCOMW1/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:24.493266","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-amsr-2-on-gcom-w1-gprof-radiometer-precipitation-profiling-l2a-1-5-hours-10-km-v08-gpm","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM AMSR-2 on GCOM-W1 (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 10 km V08 (GPM_2AGPROFGCOMW1AMSR2)","type":"dataset"},{"_score":10.457558,"_sort":[1790122524146,10.457558,2,"660c8dad-ef3f-421b-b723-864540370450"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3550185779-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550185779-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/ndh-flood-mortality-risks-distribution/maps/services","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.7927/H42F7KCP","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-floods","earth-science-population-human-dimensions-mortality","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]","temporal":"2000-01-01/2000-12-31","theme":["Earth Science"],"title":"Global Flood Mortality Risks and Distribution"},"description":"The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/78d553fb-1d93-4810-bc4c-ad6774b55bcd","harvest_record_raw":"https://catalog.data.gov/harvest_record/78d553fb-1d93-4810-bc4c-ad6774b55bcd/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H42F7KCP","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-natural-hazards-human-dimensions-floods","earth-science-population-human-dimensions-mortality","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"last_harvested_date":"2026-09-23T00:15:24.146421","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"ESDIS","slug":"global-flood-mortality-risks-and-distribution-7065e","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Global Flood Mortality Risks and Distribution","type":"dataset"},{"_score":40.974968,"_sort":[1790122523801,40.974968,0,"a7eda918-9c52-497b-89b5-3486c7a8aaed"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: \n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR-E (Aqua)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 4 x 10 km (cross-track x along-track at forward bore sight).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954516-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://aqua.nasa.gov/amsr-e","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954516-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954516-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFAQUAAMSRE_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFAQUAAMSRE_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954516-GES_DISC&q=GPM_2AGPROFAQUAAMSRE_CLIM_08","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/AMSRE/AQUA/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2002-06-01/2011-10-04","theme":["Earth Science"],"title":"GPM AMSR-E on Aqua (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 5 km V08 (GPM_2AGPROFAQUAAMSRE_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: \n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR-E (Aqua)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. \n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 4 x 10 km (cross-track x along-track at forward bore sight).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/53ef5587-9b04-45a9-9306-abdbbc16db59","harvest_record_raw":"https://catalog.data.gov/harvest_record/53ef5587-9b04-45a9-9306-abdbbc16db59/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/AMSRE/AQUA/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:23.801270","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-amsr-e-on-aqua-gprof-climate-based-radiometer-precipitation-profiling-l2-1-5-hours-5-k-9cd88","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM AMSR-E on Aqua (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 5 km V08 (GPM_2AGPROFAQUAAMSRE_CLIM)","type":"dataset"},{"_score":10.539849,"_sort":[1790122522057,10.539849,3,"f73a9a54-1e44-405c-b3b5-f2143f27bc22"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Millennium Ecosystem Assessment: MA Ecosystems provides data and information on the extent and classification of ecosystems circa 2000, including coastal, cultivated, forest and woodlands, inland water bodies, islands, marine, mountains (elevation), polar, and urban. The data set also includes socioeconomic reporting Units and the location of regional MA projects. The data were used in a number of different ways in the assessment, contributing to an understanding of how humans have altered ecosystems, how changes in ecosystem services have affected human well-being, and how ecosystem changes may affect people in future decades.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3550194184-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550194184-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/about/esdis","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.7927/H4KW5CZ6","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-ecosystems-biosphere","earth-science-environmental-impacts-human-dimensions-conservation","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-01-01/2000-12-31","theme":["Earth Science"],"title":"Millennium Ecosystem Assessment: MA Ecosystems"},"description":"The Millennium Ecosystem Assessment: MA Ecosystems provides data and information on the extent and classification of ecosystems circa 2000, including coastal, cultivated, forest and woodlands, inland water bodies, islands, marine, mountains (elevation), polar, and urban. The data set also includes socioeconomic reporting Units and the location of regional MA projects. The data were used in a number of different ways in the assessment, contributing to an understanding of how humans have altered ecosystems, how changes in ecosystem services have affected human well-being, and how ecosystem changes may affect people in future decades.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/84851450-9436-417b-9211-c3537f8fb6f4","harvest_record_raw":"https://catalog.data.gov/harvest_record/84851450-9436-417b-9211-c3537f8fb6f4/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4KW5CZ6","keyword":["earth-science-climate-change-responses-human-dimensions-climate-adaptation","earth-science-ecosystems-biosphere","earth-science-environmental-impacts-human-dimensions-conservation","earth-science-public-health-human-dimensions-food-security","earth-science-sustainability-human-dimensions"],"last_harvested_date":"2026-09-23T00:15:22.057416","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"ESDIS","slug":"millennium-ecosystem-assessment-ma-ecosystems-1a004","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Millennium Ecosystem Assessment: MA Ecosystems","type":"dataset"},{"_score":43.381405,"_sort":[1790122521361,43.381405,1,"9e4ecf9a-2bca-43b9-9175-01d9096f3b50"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Millennium Ecosystem Assessment: MA Climate and Land Cover provides data and information on global gridded climatological variables, global land cover maps, and national and international protected areas. The climatological data include the Climatic Research Unit (CRU) monthly terrestrial surface temperature and precipitation grids at 0.5 degree resolution from 1901-1996, along with temperature and precipitation grids at 10-minute resolution from the CRU and the International Water Management Institute (IWMI). The Global Land Cover 2000 (GLC2000) was produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre (JRC), which contained two levels of land cover information; detailed and regionally optimized land cover for each continent, and a less thematically detailed global land cover that harmonizes regions into one consistent product. The maps were based on daily data from the Vegetation 1 sensor on-board SPOT 4, though mapping of regions required the use of data from additional Earth observing sensors to resolve specific issues. The international and national protected areas were generated from the World Database of Protected Areas for the year 2000.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3540929415-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3540929415-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/grand-v1-reservoirs-rev01/maps/services","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.7927/H4QJ7F8M","keyword":["earth-science-environmental-impacts-human-dimensions-water-resources","earth-science-land-use-land-cover-land-surface"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1901-01-01/2000-12-31","theme":["Earth Science"],"title":"Millennium Ecosystem Assessment: MA Climate and Land Cover"},"description":"The Millennium Ecosystem Assessment: MA Climate and Land Cover provides data and information on global gridded climatological variables, global land cover maps, and national and international protected areas. The climatological data include the Climatic Research Unit (CRU) monthly terrestrial surface temperature and precipitation grids at 0.5 degree resolution from 1901-1996, along with temperature and precipitation grids at 10-minute resolution from the CRU and the International Water Management Institute (IWMI). The Global Land Cover 2000 (GLC2000) was produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre (JRC), which contained two levels of land cover information; detailed and regionally optimized land cover for each continent, and a less thematically detailed global land cover that harmonizes regions into one consistent product. The maps were based on daily data from the Vegetation 1 sensor on-board SPOT 4, though mapping of regions required the use of data from additional Earth observing sensors to resolve specific issues. The international and national protected areas were generated from the World Database of Protected Areas for the year 2000.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/970d8b84-0acc-4110-b137-e392b51952ed","harvest_record_raw":"https://catalog.data.gov/harvest_record/970d8b84-0acc-4110-b137-e392b51952ed/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4QJ7F8M","keyword":["earth-science-environmental-impacts-human-dimensions-water-resources","earth-science-land-use-land-cover-land-surface"],"last_harvested_date":"2026-09-23T00:15:21.361755","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ESDIS","slug":"millennium-ecosystem-assessment-ma-climate-and-land-cover-df92f","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Millennium Ecosystem Assessment: MA Climate and Land Cover","type":"dataset"},{"_score":18.155071,"_sort":[1790122519151,18.155071,2,"70d2bdac-b366-4e66-82da-12e67f1181b1"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Intergovernmental Panel on Climate Change (IPCC) IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1 consists of six global and regional greenhouse gases (GHGs) emissions scenarios projected from 1990 through 2100. The six alternative IPCC scenarios (IS92 A to F) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumptions affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adopted. The data set was originally produced by IPCC in 1992, and the digital version was re-edited in 2005 to resolve the discrepancies among versions of the data over years. The definitive version of this data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3551682544-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3551682544-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/about/esdis","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.7927/H41C1TT4","keyword":["earth-science-air-quality-atmosphere-emissions"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1990-01-01/2100-12-31","theme":["Earth Science"],"title":"IPCC IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1"},"description":"The Intergovernmental Panel on Climate Change (IPCC) IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1 consists of six global and regional greenhouse gases (GHGs) emissions scenarios projected from 1990 through 2100. The six alternative IPCC scenarios (IS92 A to F) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumptions affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adopted. The data set was originally produced by IPCC in 1992, and the digital version was re-edited in 2005 to resolve the discrepancies among versions of the data over years. The definitive version of this data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/443a25c8-71a7-447f-9b44-54afeb6c2b11","harvest_record_raw":"https://catalog.data.gov/harvest_record/443a25c8-71a7-447f-9b44-54afeb6c2b11/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H41C1TT4","keyword":["earth-science-air-quality-atmosphere-emissions"],"last_harvested_date":"2026-09-23T00:15:19.151061","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"ESDIS","slug":"ipcc-is92-emissions-scenarios-a-b-c-d-e-f-dataset-version-1-1-5e19d","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"IPCC IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1","type":"dataset"},{"_score":16.049171,"_sort":[1790122518389,16.049171,0,"83caa4e8-e75b-49d2-a1a3-498595f2a151"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset consists of global and regional emissions of Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), Sulfur Hexafluoride (SF6), Cholorfluorocarbons (CFCs) and Hydrochlorofluorocarbons (HCFCs) projected every 10 years beginning in 1990 through 2100. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3550193497-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550193497-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/about/esdis","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.7927/H4HD7SKJ","keyword":["earth-science-air-quality-atmosphere-emissions"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1990-01-01/2100-12-31","theme":["Earth Science"],"title":"IPCC Special Report on Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset"},"description":"The Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset consists of global and regional emissions of Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), Sulfur Hexafluoride (SF6), Cholorfluorocarbons (CFCs) and Hydrochlorofluorocarbons (HCFCs) projected every 10 years beginning in 1990 through 2100. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8d84ab0c-b268-4209-ac2b-90628ad2363c","harvest_record_raw":"https://catalog.data.gov/harvest_record/8d84ab0c-b268-4209-ac2b-90628ad2363c/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4HD7SKJ","keyword":["earth-science-air-quality-atmosphere-emissions"],"last_harvested_date":"2026-09-23T00:15:18.389883","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"ESDIS","slug":"ipcc-special-report-on-emissions-scenarios-sres-fluor-gases-emissions-dataset-f5d3a","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"IPCC Special Report on Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset","type":"dataset"},{"_score":58.225147,"_sort":[1790122517659,58.225147,0,"4b238897-5878-4e89-ae71-b4671af13a14"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a \"change consistent with warming\" or a \"change not consistent with warming\", based on information from the underlying studies.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3550191570-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550191570-ESDIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/ipcc-ar4-observed-climate-impacts/maps/services","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.7927/H4542KJV","keyword":["earth-science-atmospheric-ocean-indicators-climate-indicators-sea-level-rise","earth-science-environmental-impacts-human-dimensions","earth-science-environmental-impacts-human-dimensions-water-resources","earth-science-public-health-human-dimensions-food-security"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ESDIS"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1970-01-01/2014-12-31","theme":["Earth Science"],"title":"IPCC Fourth Assessment Report (AR4) Observed Climate Change Impacts Database"},"description":"The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a \"change consistent with warming\" or a \"change not consistent with warming\", based on information from the underlying studies.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/90b751ce-cd96-486a-a98c-43163b2737d1","harvest_record_raw":"https://catalog.data.gov/harvest_record/90b751ce-cd96-486a-a98c-43163b2737d1/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4542KJV","keyword":["earth-science-atmospheric-ocean-indicators-climate-indicators-sea-level-rise","earth-science-environmental-impacts-human-dimensions","earth-science-environmental-impacts-human-dimensions-water-resources","earth-science-public-health-human-dimensions-food-security"],"last_harvested_date":"2026-09-23T00:15:17.659256","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"ESDIS","slug":"ipcc-fourth-assessment-report-ar4-observed-climate-change-impacts-database-2572a","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"IPCC Fourth Assessment Report (AR4) Observed Climate Change Impacts Database","type":"dataset"},{"_score":40.374302,"_sort":[1790122513160,40.374302,0,"13630234-a50c-4464-bae1-d3b3dcb19417"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954558-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954558-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954558-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF14SSMI_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF14SSMI_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954558-GES_DISC&q=GPM_2AGPROFF14SSMI_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.wmo-sat.info/oscar/instruments/view/533","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMI/F14/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1997-05-07/2008-08-24","theme":["Earth Science"],"title":"GPM SSM/I on F14 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF14SSMI_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8a9302ed-7cee-4172-9fd5-a92e45d2ea86","harvest_record_raw":"https://catalog.data.gov/harvest_record/8a9302ed-7cee-4172-9fd5-a92e45d2ea86/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMI/F14/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:13.160520","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssm-i-on-f14-gprof-climate-based-radiometer-precipitation-profiling-l2-1-5-hours-12-km-c3a91","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSM/I on F14 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF14SSMI_CLIM)","type":"dataset"},{"_score":42.416122,"_sort":[1790122511045,42.416122,1,"802e1906-5fb4-4152-8023-1efe4011518a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954873-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954873-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954873-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF16SSMIS_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF16SSMIS_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954873-GES_DISC&q=GPM_2AGPROFF16SSMIS_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/ssmis.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMIS/F16/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2005-11-20/2026-09-14","theme":["Earth Science"],"title":"GPM SSMIS on F16 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF16SSMIS_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0d3802af-786f-4c39-957c-fc4f3cff25de","harvest_record_raw":"https://catalog.data.gov/harvest_record/0d3802af-786f-4c39-957c-fc4f3cff25de/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMIS/F16/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:11.045383","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssmis-on-f16-gprof-climate-based-radiometer-precipitation-profiling-1-5-hours-12-km-v0-35291","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSMIS on F16 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF16SSMIS_CLIM)","type":"dataset"},{"_score":13.699809,"_sort":[1790122510314,13.699809,1,"e20e7778-6099-4f4f-af3f-c06f47dc615b"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954578-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954578-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954578-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF16SSMIS_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF16SSMIS_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954578-GES_DISC&q=GPM_2AGPROFF16SSMIS_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/ssmis.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMIS/F16/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2014-01-31/2026-09-14","theme":["Earth Science"],"title":"GPM SSMIS on F16 (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF16SSMIS)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/61167a38-6510-4fbf-accc-f4d6bbcaaf3c","harvest_record_raw":"https://catalog.data.gov/harvest_record/61167a38-6510-4fbf-accc-f4d6bbcaaf3c/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMIS/F16/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:10.314710","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssmis-on-f16-gprof-radiometer-precipitation-profiling-l2-1-5-hours-12-km-v08-gpm_2agpr","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSMIS on F16 (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF16SSMIS)","type":"dataset"},{"_score":42.416122,"_sort":[1790122507807,42.416122,0,"3a9efab4-8b43-44ca-8934-d68f866ab368"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954729-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954729-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954729-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF17SSMIS_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF17SSMIS_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954729-GES_DISC&q=GPM_2AGPROFF17SSMIS_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/ssmis.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMIS/F17/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2006-12-14/2026-09-14","theme":["Earth Science"],"title":"GPM SSMIS on F17 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF17SSMIS_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ce91b6c5-fda9-4edd-a658-57bad78f6244","harvest_record_raw":"https://catalog.data.gov/harvest_record/ce91b6c5-fda9-4edd-a658-57bad78f6244/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMIS/F17/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:07.807488","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssmis-on-f17-gprof-climate-based-radiometer-precipitation-profiling-1-5-hours-12-km-v0-7baff","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSMIS on F17 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF17SSMIS_CLIM)","type":"dataset"},{"_score":13.746728,"_sort":[1790122506741,13.746728,1,"4cc149f8-7872-479a-8857-cc19042ae383"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954979-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954979-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954979-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF17SSMIS_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF17SSMIS_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954979-GES_DISC&q=GPM_2AGPROFF17SSMIS_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/ssmis.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMIS/F17/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2014-01-31/2026-09-14","theme":["Earth Science"],"title":"GPM SSMIS on F17 (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF17SSMIS)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a5dab165-159c-4cb5-b575-fec912bd2475","harvest_record_raw":"https://catalog.data.gov/harvest_record/a5dab165-159c-4cb5-b575-fec912bd2475/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMIS/F17/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:06.741152","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssmis-on-f17-gprof-radiometer-precipitation-profiling-l2-1-5-hours-12-km-v08-gpm_2agpr","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSMIS on F17 (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF17SSMIS)","type":"dataset"},{"_score":13.746728,"_sort":[1790122506002,13.746728,0,"49f87fe2-0f88-4e35-84c1-9d4cf922664d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954727-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954727-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954727-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF18SSMIS_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF18SSMIS_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954727-GES_DISC&q=GPM_2AGPROFF18SSMIS_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/ssmis.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMIS/F18/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2014-01-31/2026-09-14","theme":["Earth Science"],"title":"GPM SSMIS on F18 (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF18SSMIS)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/69e5529f-9d6b-4e2f-904a-79c26d048fd6","harvest_record_raw":"https://catalog.data.gov/harvest_record/69e5529f-9d6b-4e2f-904a-79c26d048fd6/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMIS/F18/GPROF/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:06.002022","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssmis-on-f18-gprof-radiometer-precipitation-profiling-l2-1-5-hours-12-km-v08-gpm_2agpr","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSMIS on F18 (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF18SSMIS)","type":"dataset"},{"_score":42.761803,"_sort":[1790122505600,42.761803,1,"8d88dc1b-6f5f-4521-9c42-a44e03a632a1"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954846-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954846-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954846-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF18SSMIS_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF18SSMIS_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954846-GES_DISC&q=GPM_2AGPROFF18SSMIS_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/ssmis.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMIS/F18/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2009-12-01/2026-09-14","theme":["Earth Science"],"title":"GPM SSMIS on F18 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF18SSMIS_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. \n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/46d1cf97-6999-4304-bbae-cca1e7883629","harvest_record_raw":"https://catalog.data.gov/harvest_record/46d1cf97-6999-4304-bbae-cca1e7883629/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMIS/F18/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:05.600331","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssmis-on-f18-gprof-climate-based-radiometer-precipitation-profiling-1-5-hours-12-km-v0-f2472","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSMIS on F18 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF18SSMIS_CLIM)","type":"dataset"},{"_score":42.190464,"_sort":[1790122504101,42.190464,1,"199092f9-50c2-4e2c-a6c4-56bd79c4f00b"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954988-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954988-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954988-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF19SSMIS_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF19SSMIS_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954988-GES_DISC&q=GPM_2AGPROFF19SSMIS_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/ssmis.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMIS/F19/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2014-12-18/2016-02-11","theme":["Earth Science"],"title":"GPM SSMIS on F19 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF19SSMIS_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d5ff5e65-a9b4-4055-b646-c11691a4ee62","harvest_record_raw":"https://catalog.data.gov/harvest_record/d5ff5e65-a9b4-4055-b646-c11691a4ee62/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMIS/F19/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-23T00:15:04.101364","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssmis-on-f19-gprof-climate-based-radiometer-precipitation-profiling-1-5-hours-12-km-v0-accae","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSMIS on F19 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V08 (GPM_2AGPROFF19SSMIS_CLIM)","type":"dataset"},{"_score":10.940724,"_sort":[1790122500156,10.940724,3,"ba9a65b5-223e-42f0-9154-085339af1831"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The MOD11C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C1.061) data product.\n\nThe MOD11C1 Version 6 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MOD11C1 product is directly derived from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.006) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MOD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. \n\nKnown Issues\n* Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2763297244-LPCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/MODIS/MOD11C1.006","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD11C1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://landweb.modaps.eosdis.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/MODLAND_val.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/centers/lp-daac","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MODIS/MOD11C1.006","keyword":["earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-land-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-02-24/2022-11-15","theme":["Earth Science"],"title":"MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006"},"description":"The MOD11C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C1.061) data product.\n\nThe MOD11C1 Version 6 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MOD11C1 product is directly derived from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.006) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MOD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. \n\nKnown Issues\n* Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/02e1b5a0-0435-4ddb-a35c-dcb1b6d42667","harvest_record_raw":"https://catalog.data.gov/harvest_record/02e1b5a0-0435-4ddb-a35c-dcb1b6d42667/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MODIS/MOD11C1.006","keyword":["earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-land-surface-temperature"],"last_harvested_date":"2026-09-23T00:15:00.156611","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS","slug":"modis-terra-land-surface-temperature-emissivity-daily-l3-global-0-05deg-cmg-v006","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006","type":"dataset"},{"_score":10.940724,"_sort":[1790122499429,10.940724,2,"b9ad22e5-283d-40bd-9991-99c82c12f07f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The MOD11C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C3 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C3.061) data product.\n\nThe MOD11C3 Version 6 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05  degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MOD11C3 product are derived by compositing and averaging the values from the corresponding month of [MOD11C1](https://doi.org/10.5067/MODIS/MOD11C1.006) daily files. Each MOD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. \n\nKnown Issues\n* Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2763297260-LPCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/MODIS/MOD11C3.006","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD11C3","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://landweb.modaps.eosdis.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/MODLAND_val.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/centers/lp-daac","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MODIS/MOD11C3.006","keyword":["earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-land-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-02-01/2022-11-30","theme":["Earth Science"],"title":"MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V006"},"description":"The MOD11C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C3 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C3.061) data product.\n\nThe MOD11C3 Version 6 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05  degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MOD11C3 product are derived by compositing and averaging the values from the corresponding month of [MOD11C1](https://doi.org/10.5067/MODIS/MOD11C1.006) daily files. Each MOD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. \n\nKnown Issues\n* Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a2b0cc60-2183-48bd-b067-fe7c23939db0","harvest_record_raw":"https://catalog.data.gov/harvest_record/a2b0cc60-2183-48bd-b067-fe7c23939db0/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MODIS/MOD11C3.006","keyword":["earth-science-surface-radiative-properties-land-surface-emissivity","earth-science-surface-thermal-properties-land-surface-land-surface-temperature"],"last_harvested_date":"2026-09-23T00:14:59.429245","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS","slug":"modis-terra-land-surface-temperature-emissivity-monthly-l3-global-0-05deg-cmg-v006","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V006","type":"dataset"},{"_score":13.608513,"_sort":[1790122495891,13.608513,1,"22c5d967-b1ee-4f33-b62c-76019e113f3c"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The MOD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09CMG Version 6.1](https://doi.org/10.5067/MODIS/MOD09CMG.061) data product.\n\nThe MOD09CMG Version 6 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. \n\nKnown Issues\n* Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range. \n* The Collection 6 MODIS Land Surface Reflectance product (MOD09) may [incorrectly flag retrievals as \u2018High Aerosol\u2019](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values.\n* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2763297176-LPCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/MODIS/MOD09CMG.006","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09CMG","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://landweb.modaps.eosdis.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/MODLAND_val.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/centers/lp-daac","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MODIS/MOD09CMG.006","keyword":["earth-science-surface-radiative-properties-land-surface-reflectance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-02-24/2023-02-17","theme":["Earth Science"],"title":"MODIS/Terra Surface Reflectance Daily L3 Global 0.05Deg CMG V006"},"description":"The MOD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09CMG Version 6.1](https://doi.org/10.5067/MODIS/MOD09CMG.061) data product.\n\nThe MOD09CMG Version 6 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. \n\nKnown Issues\n* Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range. \n* The Collection 6 MODIS Land Surface Reflectance product (MOD09) may [incorrectly flag retrievals as \u2018High Aerosol\u2019](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values.\n* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a1895fed-e043-4f29-8e39-5da0bff63817","harvest_record_raw":"https://catalog.data.gov/harvest_record/a1895fed-e043-4f29-8e39-5da0bff63817/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MODIS/MOD09CMG.006","keyword":["earth-science-surface-radiative-properties-land-surface-reflectance"],"last_harvested_date":"2026-09-23T00:14:55.891603","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS","slug":"modis-terra-surface-reflectance-daily-l3-global-0-05deg-cmg-v006","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"MODIS/Terra Surface Reflectance Daily L3 Global 0.05Deg CMG V006","type":"dataset"},{"_score":9.639812,"_sort":[1790122494234,9.639812,3,"498def32-bee1-4e71-bf1c-9e8098570cb6"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The MOD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C1.061) data product.\n\nThe MOD13C1 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.006) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\nKnown Issues\n* The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces.\n* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2763297277-LPCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/MODIS/MOD13C1.006","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13C1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/MODLAND_val.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/centers/lp-daac","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MODIS/MOD13C1.006","keyword":["earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-02-18/2023-02-17","theme":["Earth Science"],"title":"MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006"},"description":"The MOD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C1.061) data product.\n\nThe MOD13C1 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.006) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\nKnown Issues\n* The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces.\n* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e005031e-7989-4d00-b2a3-78249769f185","harvest_record_raw":"https://catalog.data.gov/harvest_record/e005031e-7989-4d00-b2a3-78249769f185/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MODIS/MOD13C1.006","keyword":["earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:14:54.234620","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS","slug":"modis-terra-vegetation-indices-16-day-l3-global-0-05deg-cmg-v006","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006","type":"dataset"},{"_score":7.913088,"_sort":[1790122492103,7.913088,5,"39d69ba7-a8f5-4288-b7d0-b46455a8362f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The MOD13C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C2 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C2.061) data product.\n\nThe MOD13C2 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.006) products that overlap the month and employs a weighted temporal average. Global MOD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\nKnown Issues\n* The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces.\n* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n\nImprovements/Changes from Previous Versions\n* The 16-day composite VI is generated using the two 8-day composite surface reflectance granules ([MOD09A1](https://doi.org/10.5067/MODIS/MOD09A1.006)) in the 16-day period.\n* This surface reflectance input is based on the minimum blue compositing approach used to generate the 8-day surface reflectance product.\n* The product format is consistent with the Version 5 product generated using the Level 2 gridded daily surface reflectance product. \n* A frequently updated long-term global CMG Average Vegetation Index product database is used to fill the gaps in the CMG product suite.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2837150321-LPCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ECOSTRESS/ECO3ANCQA.001","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://github.com/nasa/ECOSTRESS-Data-Resources","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/1573/Quick_Guide_for_Accessing_ECOSTRESS_Swath_Data_in_NASA_Earthdata_Search.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/335/ECO3ETPTJPL_ATBD_V1.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/342/ECO4ESIPTJPL_ATBD_V1.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/381/ECO3ETPTJPL_PSD_V1.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/382/ECO4ESIPTJPL_PSD_V1.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/424/ECO3ETPTJPL_User_Guide_V1.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://lpdaac.usgs.gov/documents/425/ECO4ESIPTJPL_User_Guide_V1.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/centers/lp-daac","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MODIS/MOD13C2.006","keyword":["earth-science-vegetation-biosphere-vegetation-index"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-02-01/2023-01-31","theme":["Earth Science"],"title":"MODIS/Terra Vegetation Indices Monthly L3 Global 0.05Deg CMG V006"},"description":"The MOD13C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C2 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C2.061) data product.\n\nThe MOD13C2 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.006) products that overlap the month and employs a weighted temporal average. Global MOD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. \n\nKnown Issues\n* The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces.\n* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n\nImprovements/Changes from Previous Versions\n* The 16-day composite VI is generated using the two 8-day composite surface reflectance granules ([MOD09A1](https://doi.org/10.5067/MODIS/MOD09A1.006)) in the 16-day period.\n* This surface reflectance input is based on the minimum blue compositing approach used to generate the 8-day surface reflectance product.\n* The product format is consistent with the Version 5 product generated using the Level 2 gridded daily surface reflectance product. \n* A frequently updated long-term global CMG Average Vegetation Index product database is used to fill the gaps in the CMG product suite.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b51b79e5-26a1-476d-be9c-346daf0a1ba5","harvest_record_raw":"https://catalog.data.gov/harvest_record/b51b79e5-26a1-476d-be9c-346daf0a1ba5/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MODIS/MOD13C2.006","keyword":["earth-science-vegetation-biosphere-vegetation-index"],"last_harvested_date":"2026-09-23T00:14:52.103362","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":5,"publisher":"LP DAAC;NASA/GSFC/SED/ESD/TISL/MODAPS","slug":"modis-terra-vegetation-indices-monthly-l3-global-0-05deg-cmg-v006","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"MODIS/Terra Vegetation Indices Monthly L3 Global 0.05Deg CMG V006","type":"dataset"},{"_score":10.605555,"_sort":[1790122491711,10.605555,3,"6dd7a179-e279-424f-b5bb-ae236ddc25c8"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Weather Research and Forecasting (WRF) Model IMPACTS dataset includes model data simulated by the Weather Research and Forecasting (WRF) model for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The WRF model provided simulations of the precipitation events that were observed during the campaign using initial and boundary conditions from the Global Forecast System (GFS) model and the North American Mesoscale Forecast System (NAM). The WRF IMPACTS dataset files are available from January 12, 2020, through March 4, 2023, in netCDF-3 format.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1995874860-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.5067/IMPACTS/DATA101","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://cloud.gsfc.nasa.gov/index.php?section=35","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1109/IGARSS46834.2022.9883693","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/BAMS-D-20-0246.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://espo.nasa.gov/impacts/content/IMPACTS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/wrfimpacts__1/docs/wrfimpacts_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/field-campaigns/impacts","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/micro-articles/investigation-microphysics-and-precipitation-atlantic-coast-threatening-snowstorms","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1995874860-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.eol.ucar.edu/node/15190/publications?_ga=2.3619568.1372455051.1699281084-281825729.1615488478","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.mmm.ucar.edu/weather-research-and-forecasting-model","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/IMPACTS/WRF/DATA101","keyword":["earth-science-weather-events-atmosphere-weather-climate-advisories"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 53.58893585205078, \"WestBoundingCoordinate\": -114.20199584960938, \"EastBoundingCoordinate\": -53.798004150390625, \"SouthBoundingCoordinate\": 22.970565795898438}]]","temporal":"2020-01-12/2023-03-04","theme":["Earth Science"],"title":"Weather Research and Forecasting (WRF) Model IMPACTS"},"description":"The Weather Research and Forecasting (WRF) Model IMPACTS dataset includes model data simulated by the Weather Research and Forecasting (WRF) model for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The WRF model provided simulations of the precipitation events that were observed during the campaign using initial and boundary conditions from the Global Forecast System (GFS) model and the North American Mesoscale Forecast System (NAM). The WRF IMPACTS dataset files are available from January 12, 2020, through March 4, 2023, in netCDF-3 format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4615e39a-4fd2-4825-8393-7f302df778b9","harvest_record_raw":"https://catalog.data.gov/harvest_record/4615e39a-4fd2-4825-8393-7f302df778b9/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/IMPACTS/WRF/DATA101","keyword":["earth-science-weather-events-atmosphere-weather-climate-advisories"],"last_harvested_date":"2026-09-23T00:14:51.711897","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"NASA/MSFC/GHRC","slug":"weather-research-and-forecasting-wrf-model-impacts","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Weather Research and Forecasting (WRF) Model IMPACTS","type":"dataset"},{"_score":9.903774,"_sort":[1790122482968,9.903774,1,"a245834e-eb5b-4bca-bb56-8c27fa41f97a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GOES-R PLT Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS) dataset consist of solar backscattered radiation measured by the Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS) flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign took place from March to May of 2017 in support of post-launch L1B product validation of the Advanced Baseline Image (ABI) and the Geostationary Lightning Mapper (GLM). Data files in HDF-5 format are available for March 21, 2017 through May 14, 2017.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1979115813-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.5067/GOESRPLT/DATA101","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1002/2016JD025483","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1016/j.ecss.2017.10.021","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1109/TGRS.2017.2744323","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5194/amt-9-2647-2016","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/goesrpltgcas__1/doc/goesrpltgcas_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/micro-articles/goes-r-post-launch-test-plt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1979115813-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GOESRPLT/GCAS/DATA101","keyword":["earth-science-ocean-optics-oceans-radiance","earth-science-weather-events-atmosphere-weather-climate-advisories"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -124.838, \"NorthBoundingCoordinate\": 43.6489, \"EastBoundingCoordinate\": -72.1396, \"SouthBoundingCoordinate\": 27.4778}]]","temporal":"2017-03-21/2017-05-14","theme":["Earth Science"],"title":"GOES-R PLT Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS)"},"description":"The GOES-R PLT Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS) dataset consist of solar backscattered radiation measured by the Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS) flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign took place from March to May of 2017 in support of post-launch L1B product validation of the Advanced Baseline Image (ABI) and the Geostationary Lightning Mapper (GLM). Data files in HDF-5 format are available for March 21, 2017 through May 14, 2017.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f7bf1a2f-9428-43d3-ab53-67bdeb3d3d6e","harvest_record_raw":"https://catalog.data.gov/harvest_record/f7bf1a2f-9428-43d3-ab53-67bdeb3d3d6e/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GOESRPLT/GCAS/DATA101","keyword":["earth-science-ocean-optics-oceans-radiance","earth-science-weather-events-atmosphere-weather-climate-advisories"],"last_harvested_date":"2026-09-23T00:14:42.968634","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/MSFC/GHRC","slug":"goes-r-plt-geostationary-coastal-and-air-pollution-event-geo-cape-airborne-simulator-gcas","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GOES-R PLT Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator (GCAS)","type":"dataset"},{"_score":6.963436,"_sort":[1790122482235,6.963436,3,"073c7116-b1ed-4f0e-9f8b-bca85e6360af"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The High Impact Weather Assessment Toolkit (HIWAT) uses a mesoscale numerical weather prediction model and the Global Precipitation Measurement (GPM) constellation of satellites. The toolkit includes a suite of ensemble model forecasts to constrain uncertainties and provide a probabilistic forecast for improved decision-making. The toolkit provides outlooks for lightning strikes, high-impact winds, high rainfall rates, hail damage, and other weather events. The toolkit provides a 54-hour probabilistic forecast over Nepal and Bangladesh along with parts of northeast India (i.e., the Hindu Kush Himalayan region). HIWAT will also support threat assessments, such as thunderstorm intensity, using GPM and impact assessments using Landsat/MODIS land imagery to identify damage scars. The dataset files are available from April 2, 2017, through October 2, 2022, in netCDF-3 format.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2756158683-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/BAMS-D-21-0260.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/hiwat__1/docs/hiwat_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/api/citations/20210000535/downloads/CH15_SHKH-2020-0601_accepted.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2756158683-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://servir.icimod.org/science-applications/high-impact-weather-assessment-toolkit/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MODEL/HIWAT/DATA101","keyword":["earth-science-atmospheric-ocean-indicators-climate-indicators-cloud-indicators","earth-science-atmospheric-winds-atmosphere-upper-level-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-amount","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-oceans-liquid-precipitation","earth-science-weather-events-atmosphere-stability-severe-weather-indices"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 45.951, \"WestBoundingCoordinate\": 60.562, \"EastBoundingCoordinate\": 111.438, \"SouthBoundingCoordinate\": 10.632}]]","temporal":"2017-04-02/2022-10-02","theme":["Earth Science"],"title":"High-Impact Weather Assessment Toolkit (HIWAT)"},"description":"The High Impact Weather Assessment Toolkit (HIWAT) uses a mesoscale numerical weather prediction model and the Global Precipitation Measurement (GPM) constellation of satellites. The toolkit includes a suite of ensemble model forecasts to constrain uncertainties and provide a probabilistic forecast for improved decision-making. The toolkit provides outlooks for lightning strikes, high-impact winds, high rainfall rates, hail damage, and other weather events. The toolkit provides a 54-hour probabilistic forecast over Nepal and Bangladesh along with parts of northeast India (i.e., the Hindu Kush Himalayan region). HIWAT will also support threat assessments, such as thunderstorm intensity, using GPM and impact assessments using Landsat/MODIS land imagery to identify damage scars. The dataset files are available from April 2, 2017, through October 2, 2022, in netCDF-3 format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d85c591d-d1a0-48f0-b969-7694ea1732b7","harvest_record_raw":"https://catalog.data.gov/harvest_record/d85c591d-d1a0-48f0-b969-7694ea1732b7/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MODEL/HIWAT/DATA101","keyword":["earth-science-atmospheric-ocean-indicators-climate-indicators-cloud-indicators","earth-science-atmospheric-winds-atmosphere-upper-level-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-amount","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-oceans-liquid-precipitation","earth-science-weather-events-atmosphere-stability-severe-weather-indices"],"last_harvested_date":"2026-09-23T00:14:42.235696","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"NASA/MSFC/GHRC","slug":"high-impact-weather-assessment-toolkit-hiwat","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"High-Impact Weather Assessment Toolkit (HIWAT)","type":"dataset"},{"_score":9.510666,"_sort":[1790122481517,9.510666,3,"df701d86-9a9c-431e-9ff9-5d78cbd793a0"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GOES-R PLT Mission Reports dataset consists of various reports filed by the scientists during the GOES-R Post Launch Test (PLT) field campaign including flight reports, weather forecasts, mission scientist reports, and plan-of-day reports. The campaign took place from March to May of 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The GOES-R PLT Mission Reports dataset contains reports from March 13 through May 17, 2017 in PDF, PNG, Microsoft Excel and Word (.xlsx and .docx) format, and KMZ format for display in Google Earth.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1995569043-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/goesrpltmisrep__1/doc/goesrpltmisrep_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/field-campaigns/goes-r_plt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/micro-articles/goes-r-post-launch-test-plt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1995569043-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.goes-r.gov/products/RIMPs/RIMP_ABI-L2_DSR-RSR_v1.0.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.5067/GOESRPLT/REPORTS/DATA101","keyword":["earth-science-platform-characteristics-spectral-engineering-flight-data-logs","earth-science-weather-events-atmosphere-weather-climate-advisories"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -124.625, \"NorthBoundingCoordinate\": 43.573, \"EastBoundingCoordinate\": -72.202, \"SouthBoundingCoordinate\": 26.449}]]","temporal":"2017-03-13/2017-05-17","theme":["Earth Science"],"title":"GOES-R PLT Mission Reports"},"description":"The GOES-R PLT Mission Reports dataset consists of various reports filed by the scientists during the GOES-R Post Launch Test (PLT) field campaign including flight reports, weather forecasts, mission scientist reports, and plan-of-day reports. The campaign took place from March to May of 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The GOES-R PLT Mission Reports dataset contains reports from March 13 through May 17, 2017 in PDF, PNG, Microsoft Excel and Word (.xlsx and .docx) format, and KMZ format for display in Google Earth.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d31092dd-d58c-49c4-8987-21d86af9c5c3","harvest_record_raw":"https://catalog.data.gov/harvest_record/d31092dd-d58c-49c4-8987-21d86af9c5c3/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GOESRPLT/REPORTS/DATA101","keyword":["earth-science-platform-characteristics-spectral-engineering-flight-data-logs","earth-science-weather-events-atmosphere-weather-climate-advisories"],"last_harvested_date":"2026-09-23T00:14:41.517330","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"NASA/MSFC/GHRC","slug":"goes-r-plt-mission-reports","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GOES-R PLT Mission Reports","type":"dataset"},{"_score":11.323688,"_sort":[1790122478280,11.323688,4,"1e50fc1e-57d9-486e-b4a3-6a35d114fc4f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 3.2 has been superseded by Version 3.3 (10.5067/MEASURES/GPCP/DATA307).\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Exchange (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program.  As the follow on to the GPCP Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.2) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets.  The GPCPV3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. Additional products may be added, which consist of (1) 0.5-degree pentad and (2) 0.1-degree 3-hourly.  All GPCPV3 products will be internally consistent.  The Daily product spans June 2000-December 2020. Inputs consist of GPM IMERG in the span 55\u00b0N-S, and TOVS/AIRS estimates, adjusted climatologically to IMERG, outside 55\u00b0N-S.  The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5\u00b0 grid box.  In addition to the final precipitation field, probability of liquid phase estimates are provided globally.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2211141201-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPCPDAY_3.2.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GPCPDAY_V32.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/GPCP_ATBD_V3.2_Daily.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/Release_Notes.GPCPV3.2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://giovanni.gsfc.nasa.gov/giovanni/#dataKeyword=GPCP%20daily%20V3.2","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/GPCP/GPCPDAY.3.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/GPCP/GPCPDAY.3.2/doc/README.GPCPV3.2_Daily.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/opendap/GPCP/GPCPDAY.3.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2211141201-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MEASURES/GPCP/DATA305","keyword":["earth-science-precipitation-atmosphere","earth-science-precipitation-atmosphere-precipitation-rate"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-06-01/2021-09-30","theme":["Earth Science"],"title":"GPCP Precipitation Level 3 Daily 0.5-Degree V3.2 (GPCPDAY) at GES DISC"},"description":"Version 3.2 has been superseded by Version 3.3 (10.5067/MEASURES/GPCP/DATA307).\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Exchange (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program.  As the follow on to the GPCP Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.2) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets.  The GPCPV3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. Additional products may be added, which consist of (1) 0.5-degree pentad and (2) 0.1-degree 3-hourly.  All GPCPV3 products will be internally consistent.  The Daily product spans June 2000-December 2020. Inputs consist of GPM IMERG in the span 55\u00b0N-S, and TOVS/AIRS estimates, adjusted climatologically to IMERG, outside 55\u00b0N-S.  The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5\u00b0 grid box.  In addition to the final precipitation field, probability of liquid phase estimates are provided globally.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/312f1e80-1f5e-42b5-844e-80ef82d473bf","harvest_record_raw":"https://catalog.data.gov/harvest_record/312f1e80-1f5e-42b5-844e-80ef82d473bf/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MEASURES/GPCP/DATA305","keyword":["earth-science-precipitation-atmosphere","earth-science-precipitation-atmosphere-precipitation-rate"],"last_harvested_date":"2026-09-23T00:14:38.280529","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":4,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpcp-precipitation-level-3-daily-0-5-degree-v3-2-gpcpday-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPCP Precipitation Level 3 Daily 0.5-Degree V3.2 (GPCPDAY) at GES DISC","type":"dataset"},{"_score":13.699809,"_sort":[1790122467459,13.699809,1,"7327aac6-4daf-4601-b633-4408ca74bf86"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"We will have combined airborne and field sampling at PACE overpass time over two sampling periods October 2024 and May 2025, spanning a wide range of aerosol and ocean states for Monterey Bay, California. Likely potential aerosol conditions include, but not limited to, maritime aerosol, wildfire smoke, long range Asian dust transport, and clear air (Zhao et al., 2013, Lewis et al., 2010, Mardi et al., 2018, Allan et al., 2004, VanCuren 2003). Expected maritime conditions include, but are not limited to, low productivity cold waters, algal blooms, riverine outflow, and turbid waters. The study site has dramatic conditions exacerbated by the changing climate and a recent history of significant fire seasons (Filoncyk et al., 2022), extreme precipitation conditions, namely drought, atmospheric rivers and subsequent changes in riverine outflow, and other climate change impacts such as harmful algal blooms and far-reaching riverine plumes. We achieve PACE validation with hyperspectral (e.g., HyperPro II water optical profiling and 4STAR-B atmospheric transmittance) and aligned contemporary radiometric measurements (C-AIR water-leaving radiance from aircraft and C-OPS water optical profiling). The latter contemporary ocean color detectors have much higher dynamic range than OCI. This unique combination of airborne and surface and profiling instrumentation and water sampling can provide high-accuracy validation of the PACE mission sensors in a globally-representative range of oceanic conditions. The repeated airborne observations can provide the calibration and validation over larger areas and time by collecting measurements over a larger spatial domain during a satellite overpass and different seasons, overcoming the problem of limited spatial coverage presented by using solely ship stations and moored buoy systems.The AOD data from this project is archived at the NASA Langley www-air site in netcdf format.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3394587427-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://seabass.gsfc.nasa.gov/experiment/PVST_AIRSHARP/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3394587427-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SeaBASS/PVST_AIRSHARP/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180.0, \"NorthBoundingCoordinate\": 90.0, \"SouthBoundingCoordinate\": -90.0, \"WestBoundingCoordinate\": -180.0}]]","temporal":"2023-12-31/2026-09-14","theme":["Earth Science"],"title":"Airborne asSessment of Hyperspectral Aerosol optical depth and water-leaving Reflectance Product Performance for PACE"},"description":"We will have combined airborne and field sampling at PACE overpass time over two sampling periods October 2024 and May 2025, spanning a wide range of aerosol and ocean states for Monterey Bay, California. Likely potential aerosol conditions include, but not limited to, maritime aerosol, wildfire smoke, long range Asian dust transport, and clear air (Zhao et al., 2013, Lewis et al., 2010, Mardi et al., 2018, Allan et al., 2004, VanCuren 2003). Expected maritime conditions include, but are not limited to, low productivity cold waters, algal blooms, riverine outflow, and turbid waters. The study site has dramatic conditions exacerbated by the changing climate and a recent history of significant fire seasons (Filoncyk et al., 2022), extreme precipitation conditions, namely drought, atmospheric rivers and subsequent changes in riverine outflow, and other climate change impacts such as harmful algal blooms and far-reaching riverine plumes. We achieve PACE validation with hyperspectral (e.g., HyperPro II water optical profiling and 4STAR-B atmospheric transmittance) and aligned contemporary radiometric measurements (C-AIR water-leaving radiance from aircraft and C-OPS water optical profiling). The latter contemporary ocean color detectors have much higher dynamic range than OCI. This unique combination of airborne and surface and profiling instrumentation and water sampling can provide high-accuracy validation of the PACE mission sensors in a globally-representative range of oceanic conditions. The repeated airborne observations can provide the calibration and validation over larger areas and time by collecting measurements over a larger spatial domain during a satellite overpass and different seasons, overcoming the problem of limited spatial coverage presented by using solely ship stations and moored buoy systems.The AOD data from this project is archived at the NASA Langley www-air site in netcdf format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b67b816a-9c82-4fa2-8c4e-7d4ee0092733","harvest_record_raw":"https://catalog.data.gov/harvest_record/b67b816a-9c82-4fa2-8c4e-7d4ee0092733/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SeaBASS/PVST_AIRSHARP/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"last_harvested_date":"2026-09-23T00:14:27.459949","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS","slug":"airborne-assessment-of-hyperspectral-aerosol-optical-depth-and-water-leaving-reflectance-p","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Airborne asSessment of Hyperspectral Aerosol optical depth and water-leaving Reflectance Product Performance for PACE","type":"dataset"},{"_score":11.010476,"_sort":[1790122465669,11.010476,1,"ee2382c4-88cf-40c7-be90-fc17f636a9a5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C) \n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask) \n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag) \n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- sstref \u2014 Sea Surface Temperature reference (\u00b0C) \n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1641945527-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-2/Aqua-MODIS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1641945527-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/MODIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Aqua","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1641945527-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90, \"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90}]]","temporal":"2002-07-04/2026-09-14","theme":["Earth Science"],"title":"Aqua MODIS Level-2 Regional  11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C) \n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask) \n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag) \n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- sstref \u2014 Sea Surface Temperature reference (\u00b0C) \n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b66276ab-1b92-4ff2-bc32-d7d146f17492","harvest_record_raw":"https://catalog.data.gov/harvest_record/b66276ab-1b92-4ff2-bc32-d7d146f17492/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1641945527-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:14:25.669301","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"aqua-modis-level-2-regional-11m-day-night-sea-surface-temperature-sst-near-real-time-nrt-d-bce3c","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Aqua MODIS Level-2 Regional  11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0","type":"dataset"},{"_score":12.586679,"_sort":[1790122465315,12.586679,0,"8583da51-4b18-4a5a-92d7-fc1c18c4aef3"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"SST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C)\n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask)\n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions)\n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)\n- sst \u2014 Sea Surface Temperature (\u00b0C)\n- sstref \u2014 Sea Surface Temperature reference (\u00b0C)\n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1615905765-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-2/Aqua-MODIS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1615905765-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/MODIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Aqua","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1615905765-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180, \"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2002-07-04/2026-09-14","theme":["Earth Science"],"title":"Aqua MODIS Level-2 Regional  11\u00b5m Daytime Sea Surface Temperature (SST) Data, version R2019.0"},"description":"SST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C)\n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask)\n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions)\n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)\n- sst \u2014 Sea Surface Temperature (\u00b0C)\n- sstref \u2014 Sea Surface Temperature reference (\u00b0C)\n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/79ce40b7-0d08-441e-b364-447c1698f9fa","harvest_record_raw":"https://catalog.data.gov/harvest_record/79ce40b7-0d08-441e-b364-447c1698f9fa/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1615905765-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:14:25.315663","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"aqua-modis-level-regional-regional-11m-day-night-sea-surface-temperature-sst-data-version--1f7f0","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Aqua MODIS Level-2 Regional  11\u00b5m Daytime Sea Surface Temperature (SST) Data, version R2019.0","type":"dataset"},{"_score":12.586679,"_sort":[1790122464605,12.586679,1,"ba893c83-25d4-4755-9542-29c3ea0182fd"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1641945804-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Binned/Aqua-MODIS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1641945804-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/MODIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Aqua","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1641945804-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90, \"WestBoundingCoordinate\": -180}]]","temporal":"2002-07-04/2026-09-14","theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Binned 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2e4fb9a3-9110-4982-a737-b652c576ef5e","harvest_record_raw":"https://catalog.data.gov/harvest_record/2e4fb9a3-9110-4982-a737-b652c576ef5e/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1641945804-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:14:24.605671","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"aqua-modis-level-3-global-binned-11m-day-night-sea-surface-temperature-sst-near-real-time--8d710","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Binned 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0","type":"dataset"},{"_score":12.251391,"_sort":[1790122464244,12.251391,1,"4f7fcfd5-c98d-4c15-b44c-bc06fa462a66"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nNSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C)\n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1641945661-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Binned/Aqua-MODIS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1641945661-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/MODIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Aqua","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1641945661-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"SouthBoundingCoordinate\": -90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180}]]","temporal":"2002-07-04/2026-09-14","theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Binned 11\u00b5m Nighttime Sea Surface Temperature NSST) - Near Real-time (NRT) Data, version R2019.0"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nNSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C)\n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/99ce5143-dd7f-4f5b-8492-e90df7148d39","harvest_record_raw":"https://catalog.data.gov/harvest_record/99ce5143-dd7f-4f5b-8492-e90df7148d39/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1641945661-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:14:24.244037","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"aqua-modis-level-3-global-binned-11m-nighttime-sea-surface-temperature-nsst-near-real-time-6373c","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Binned 11\u00b5m Nighttime Sea Surface Temperature NSST) - Near Real-time (NRT) Data, version R2019.0","type":"dataset"},{"_score":12.544986,"_sort":[1790122463541,12.544986,1,"527a4c45-e4a5-4213-b3e4-975d911b74da"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1641945980-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Mapped/Aqua-MODIS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/opendap/MODISA/L3SMI/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1641945980-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/MODIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Aqua","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1641945980-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"SouthBoundingCoordinate\": -90, \"EastBoundingCoordinate\": 180, \"NorthBoundingCoordinate\": 90}]]","temporal":"2002-07-04/2026-09-14","theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Mapped 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4f5441f4-9b67-41c3-b4fd-2e37476691c4","harvest_record_raw":"https://catalog.data.gov/harvest_record/4f5441f4-9b67-41c3-b4fd-2e37476691c4/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1641945980-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:14:23.541725","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"aqua-modis-level-3-global-mapped-11m-day-night-sea-surface-temperature-sst-near-real-time--03fb4","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Mapped 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0","type":"dataset"},{"_score":13.944383,"_sort":[1790122463189,13.944383,1,"fc1ae77b-0043-4927-a032-d3d6f7c5105d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"NSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C) \n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1615929573-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Mapped/Aqua-MODIS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/opendap/MODISA/L3SMI/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1615929573-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/MODIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Aqua","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1615929573-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90, \"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90}]]","temporal":"2002-07-04/2026-09-14","theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Mapped 11\u00b5m Nighttime Sea Surface Temperature (NSST) Data, version R2019.0"},"description":"NSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C) \n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/91a99fc6-68fa-4b40-afed-eb9652bb80c1","harvest_record_raw":"https://catalog.data.gov/harvest_record/91a99fc6-68fa-4b40-afed-eb9652bb80c1/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1615929573-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:14:23.189998","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"aqua-modis-level-3-global-mapped-11m-nighttime-sea-surface-temperature-nsst-data-version-r-ebed6","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Aqua MODIS Level-3 Global Mapped 11\u00b5m Nighttime Sea Surface Temperature (NSST) Data, version R2019.0","type":"dataset"},{"_score":14.644705,"_sort":[1790122436887,14.644705,1,"69f48f63-7a42-4ed7-ab20-1ef3be9c4b9d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"ISCCP_ICESNOW_NAT_1 is the International Satellite Cloud Climatology Project (ISCCP) Ice Snow Product in Native Data Format data set. It is a merged product containing separate snow and sea ice data sets. The values given are fractional coverage for 5-day intervals. Data collection for this data set is complete. \r\n\r\nThe data were collected on a global equal-area grid with the cell area equivalent to 1 degree latitude/longitude at the equator. The grid began at the South Pole with the intersection of the Greenwich meridian (0 degree longitude) and the South Pole as a cell corner. Ice/Snow Data Set contents included 5-day averages of snow and sea ice fractional coverage. Snow is deduced from visible satellite imagery plus ground data while sea ice was deduced from ship/shore and visible satellite imagery, and microwave measurements. \r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP), to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis products were used to improve the understanding and modeling of the effects of clouds on climate. The ISCCP version of the ice/snow data set included only information concerning fractional coverage. The version actually used in the cloud analysis was changed in two ways: reductions to ice/snow presence and creation of margin zones in the data. The first of these was simply the process of converting the coded parameters in the original data set to code values that indicate only the presence or absence of sea ice and/or snow. The latter process filled in nearby grid cells in the data to indicate proximity to snow or sea ice covered locations. The second change is not included in the archived version of this data.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3880704205-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/abstract_ISCCP_ICESNOW_NAT.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/isccp_project.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_icesnow_read.c","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/readme/readme_isccp_icesnow_nat.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/ISCCP","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://climatedataguide.ucar.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880704205-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ISCCP/ICESNOW_NAT","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://earthdata.nasa.gov/esds/competitive-programs/measures/data-analysis-for-isccp","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/ShadowsDoubt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/StarsCloudsCrops","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/docs/documents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/errors.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/newalg.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/products/products.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/aura/science-and-data/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880704205-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/learn/sensing-our-planet/but-the-winds-but-the-spaces","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.giss.nasa.gov/tools/panoply/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/ISCCP/ICESNOW_NAT","keyword":["earth-science-sea-ice-cryosphere","earth-science-sea-ice-oceans","earth-science-snow-ice-cryosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"Boundary\": {\"Points\": [{\"Latitude\": -90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": -180}]}}]]","temporal":"1983-07-03/2009-12-28","theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Ice Snow Product in Native Data Format"},"description":"ISCCP_ICESNOW_NAT_1 is the International Satellite Cloud Climatology Project (ISCCP) Ice Snow Product in Native Data Format data set. It is a merged product containing separate snow and sea ice data sets. The values given are fractional coverage for 5-day intervals. Data collection for this data set is complete. \r\n\r\nThe data were collected on a global equal-area grid with the cell area equivalent to 1 degree latitude/longitude at the equator. The grid began at the South Pole with the intersection of the Greenwich meridian (0 degree longitude) and the South Pole as a cell corner. Ice/Snow Data Set contents included 5-day averages of snow and sea ice fractional coverage. Snow is deduced from visible satellite imagery plus ground data while sea ice was deduced from ship/shore and visible satellite imagery, and microwave measurements. \r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP), to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis products were used to improve the understanding and modeling of the effects of clouds on climate. The ISCCP version of the ice/snow data set included only information concerning fractional coverage. The version actually used in the cloud analysis was changed in two ways: reductions to ice/snow presence and creation of margin zones in the data. The first of these was simply the process of converting the coded parameters in the original data set to code values that indicate only the presence or absence of sea ice and/or snow. The latter process filled in nearby grid cells in the data to indicate proximity to snow or sea ice covered locations. The second change is not included in the archived version of this data.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4a751f99-ce56-4511-831b-0f5d3b486eb1","harvest_record_raw":"https://catalog.data.gov/harvest_record/4a751f99-ce56-4511-831b-0f5d3b486eb1/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ISCCP/ICESNOW_NAT","keyword":["earth-science-sea-ice-cryosphere","earth-science-sea-ice-oceans","earth-science-snow-ice-cryosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover"],"last_harvested_date":"2026-09-23T00:13:56.887547","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"international-satellite-cloud-climatology-project-isccp-ice-snow-product-in-native-data-fo","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Ice Snow Product in Native Data Format","type":"dataset"},{"_score":11.584117,"_sort":[1790122436491,11.584117,1,"22d2b9dd-cb12-420f-8ebb-51300421f506"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"ISCCP_D1_1 is the International Satellite Cloud Climatology Project (ISCCP) Stage D1 3-Hourly Cloud Products - Revised Algorithm data set in Hierarchical Data Format. This data set contains 3-hourly, 280 KM equal-area grid data from various polar and geostationary satellites. The Gridded Cloud Product contents are spatial averages of DX quantities and statistical summaries, including properties of cloud types. Satellites are merged into a global grid. Atmosphere and surface properties from TOVS are appended. Data collection for this data set is complete. \r\n\r\nISCCP, the first project of the World Climate Research Program (WCRP), was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to: produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nStarting in 1983 an international group of institutions collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and interannual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onward) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nThe ISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis was performed separately for each satellite radiance data set and the results were reported in the Stage DX data product, which had a nominal resolution of 30 km and 3 hours. The Stage D1 product was produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product was produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3880704231-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/base_isccp_d1_d2_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/isccp_project.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_d1_read.c","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_d1_read.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/readme/readme_isccp_d1.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/ISCCP","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://climatedataguide.ucar.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880704231-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ISCCP/D1","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://earthdata.nasa.gov/esds/competitive-programs/measures/data-analysis-for-isccp","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/ShadowsDoubt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/StarsCloudsCrops","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/docs/documents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/errors.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/newalg.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/products/products.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/aura/science-and-data/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880704231-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/learn/sensing-our-planet/but-the-winds-but-the-spaces","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.giss.nasa.gov/tools/panoply/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/ISCCP/D1","keyword":["earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species","earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-optical-depth-thickness","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-snow-ice-cryosphere-ice-extent","earth-science-snow-ice-cryosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-ice-extent","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"Boundary\": {\"Points\": [{\"Latitude\": -90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": -180}]}}]]","temporal":"1983-07-01/2009-12-31","theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage D1 3-Hourly Cloud Products - Revised Algorithm in Hierarchical Data Format"},"description":"ISCCP_D1_1 is the International Satellite Cloud Climatology Project (ISCCP) Stage D1 3-Hourly Cloud Products - Revised Algorithm data set in Hierarchical Data Format. This data set contains 3-hourly, 280 KM equal-area grid data from various polar and geostationary satellites. The Gridded Cloud Product contents are spatial averages of DX quantities and statistical summaries, including properties of cloud types. Satellites are merged into a global grid. Atmosphere and surface properties from TOVS are appended. Data collection for this data set is complete. \r\n\r\nISCCP, the first project of the World Climate Research Program (WCRP), was established in 1982 (WMO-35 1982, Schiffer and Rossow 1983) to: produce a global, reduced resolution, calibrated and normalized radiance data set containing basic information on the properties of the atmosphere from which cloud parameters can be derived; stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models; and promote research using ISCCP data that contributes to improved understanding of the Earth's radiation budget and hydrological cycle. \r\n\r\nStarting in 1983 an international group of institutions collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and interannual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onward) the analysis also concerns improving understanding of clouds in the global hydrological cycle. \r\n\r\nThe ISCCP analysis combined satellite-measured radiances (Stage B3 data, Schiffer and Rossow 1985), Rossow et al. 1987) with the TOVS atmospheric temperature-humidity and ice/snow correlative data sets to obtain information about clouds and the surface. The analysis method first determined the presence of absence of clouds in each individual image pixel and retrieves the radiometric properties of the cloud for each cloudy pixel and of the surface for each clear pixel. The pixel analysis was performed separately for each satellite radiance data set and the results were reported in the Stage DX data product, which had a nominal resolution of 30 km and 3 hours. The Stage D1 product was produced by summarizing the pixel-level results every 3 hours on an equal-area map with 280 km resolution and merging the results from separate satellites with the atmospheric and ice/snow data sets to produce global coverage at each time. The Stage D2 data product was produced by averaging the Stage D1 data over each month, first at each of the eight three hour time intervals and then over all time intervals.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/046bb7d7-9d91-4186-bf83-ad1df6f77066","harvest_record_raw":"https://catalog.data.gov/harvest_record/046bb7d7-9d91-4186-bf83-ad1df6f77066/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ISCCP/D1","keyword":["earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species","earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-optical-depth-thickness","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-snow-ice-cryosphere-ice-extent","earth-science-snow-ice-cryosphere-snow-cover","earth-science-snow-ice-terrestrial-hydrosphere-ice-extent","earth-science-snow-ice-terrestrial-hydrosphere-snow-cover"],"last_harvested_date":"2026-09-23T00:13:56.491259","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"international-satellite-cloud-climatology-project-isccp-stage-d1-3-hourly-cloud-products-r","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) Stage D1 3-Hourly Cloud Products - Revised Algorithm in Hierarchical Data Format","type":"dataset"},{"_score":12.473566,"_sort":[1790122436099,12.473566,3,"712f534b-b490-4a91-8c7a-b05499cc840f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"ISCCP_TOVS_NAT_1 is the International Satellite Cloud Climatology Project (ISCCP) TIROS Operational Vertical Sounder (TOVS) data set in the Native Data Format. It is a daily, global description of the ozone, temperature, and humidity distributions obtained from the analysis of data from the TOVS system. The TOVS data set contents include atmosphere and surface data including temperature structure, water, and ozone abundances obtained from the TOVS product and supplemented by two climatologies. The ISCCP_TOVS_NAT data set contains information concerning the atmospheric temperature and humidity profiles as well as the ozone column abundance. Data collection for this data set is complete.\r\n\r\nThis data set is composed of 3 types of data files: CLIM MONTHLY, which contains the monthly climatological data obtained from balloon observations; TOVS MONTHLY, which contains the monthly climatological data computed from the daily TOVS values; and TOVS DAILY, which contains the daily composite of the TOVS Sounding Product. The data was collected on a global equal-area grid with the cell area equivalent to 2.5 degrees latitude/longitude at the equator. The grid began at the South Pole with the intersection of the Greenwich meridian (0 deg. longitude) and the South Pole as a cell corner. \r\n\r\nThe TOVS system flew on the NOAA Operational Polar Orbiting Satellite series. Measurements from the High Resolution Infrared Radiation Sounder (HIRS/2), the Stratospheric Sounding Unit (SSU), and the Microwave Sounding Unit (MSU) were processed by NOAA to produce the TOVS Sounding Product.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP), to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis products were used to improve the understanding and modeling of the effects of clouds on climate. The ISCCP version of the ice/snow data set included only information concerning fractional coverage. The version actually used in the cloud analysis was changed in two ways: reductions to ice/snow presence and creation of margin zones in the data. The first of these was simply the process of converting the coded parameters in the original data set to code values that indicate only the presence or absence of sea ice and/or snow. The latter process filled in nearby grid cells in the data to indicate proximity to snow or sea ice covered locations. The second change is not included in the archived version of this data.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3880704263-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/abstract_ISCCP_TOVS_NAT.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/guide/isccp_project.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_tovs_read.c","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/read_software/isccp_tovs_read.f","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/isccp/readme/readme_isccp_tovs_nat.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/ISCCP","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://climatedataguide.ucar.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880704263-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ISCCP/TOVS_NAT","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://earthdata.nasa.gov/esds/competitive-programs/measures/data-analysis-for-isccp","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/ShadowsDoubt","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/features/StarsCloudsCrops","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/docs/documents.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/errors.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/newalg.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://isccp.giss.nasa.gov/products/products.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/aura/science-and-data/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880704263-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/learn/sensing-our-planet/but-the-winds-but-the-spaces","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.giss.nasa.gov/tools/panoply/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/ISCCP/TOVS_NAT","keyword":["earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"Boundary\": {\"Points\": [{\"Latitude\": -90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": 180}, {\"Latitude\": 90, \"Longitude\": -180}, {\"Latitude\": -90, \"Longitude\": -180}]}}]]","temporal":"1980-01-01/2009-12-31","theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) TOVS in Native Data Format"},"description":"ISCCP_TOVS_NAT_1 is the International Satellite Cloud Climatology Project (ISCCP) TIROS Operational Vertical Sounder (TOVS) data set in the Native Data Format. It is a daily, global description of the ozone, temperature, and humidity distributions obtained from the analysis of data from the TOVS system. The TOVS data set contents include atmosphere and surface data including temperature structure, water, and ozone abundances obtained from the TOVS product and supplemented by two climatologies. The ISCCP_TOVS_NAT data set contains information concerning the atmospheric temperature and humidity profiles as well as the ozone column abundance. Data collection for this data set is complete.\r\n\r\nThis data set is composed of 3 types of data files: CLIM MONTHLY, which contains the monthly climatological data obtained from balloon observations; TOVS MONTHLY, which contains the monthly climatological data computed from the daily TOVS values; and TOVS DAILY, which contains the daily composite of the TOVS Sounding Product. The data was collected on a global equal-area grid with the cell area equivalent to 2.5 degrees latitude/longitude at the equator. The grid began at the South Pole with the intersection of the Greenwich meridian (0 deg. longitude) and the South Pole as a cell corner. \r\n\r\nThe TOVS system flew on the NOAA Operational Polar Orbiting Satellite series. Measurements from the High Resolution Infrared Radiation Sounder (HIRS/2), the Stratospheric Sounding Unit (SSU), and the Microwave Sounding Unit (MSU) were processed by NOAA to produce the TOVS Sounding Product.\r\n\r\nISCCP was the first project of the World Climate Research Program (WCRP), to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis products were used to improve the understanding and modeling of the effects of clouds on climate. The ISCCP version of the ice/snow data set included only information concerning fractional coverage. The version actually used in the cloud analysis was changed in two ways: reductions to ice/snow presence and creation of margin zones in the data. The first of these was simply the process of converting the coded parameters in the original data set to code values that indicate only the presence or absence of sea ice and/or snow. The latter process filled in nearby grid cells in the data to indicate proximity to snow or sea ice covered locations. The second change is not included in the archived version of this data.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/fbada795-568e-419d-b0b2-19187b91de5e","harvest_record_raw":"https://catalog.data.gov/harvest_record/fbada795-568e-419d-b0b2-19187b91de5e/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ISCCP/TOVS_NAT","keyword":["earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles"],"last_harvested_date":"2026-09-23T00:13:56.099030","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"NASA/LARC/SD/ASDC","slug":"international-satellite-cloud-climatology-project-isccp-tovs-in-native-data-format","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"International Satellite Cloud Climatology Project (ISCCP) TOVS in Native Data Format","type":"dataset"},{"_score":49.442345,"_sort":[1790122425557,49.442345,1,"8e2ca0e6-9ed1-47a2-839a-45fc5387c77e"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"SOCCOM (Southern Ocean Carbon and Climate Observations and Modeling project) is a NSF project sampling the Southern Ocean and its influence on climate.Additional Data LinksCLIVAR P16S_2014 Pigment AnalysisCLIVAR P16S_2014 POC dataCLIVAR P16S_2014 Supporting Documentation","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633360663-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://seabass.gsfc.nasa.gov/experiment/SOCCOM/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633360663-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SeaBASS/SOCCOM/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180.0, \"NorthBoundingCoordinate\": 90.0, \"SouthBoundingCoordinate\": -90.0, \"WestBoundingCoordinate\": -180.0}]]","temporal":"2014-12-05/2026-09-14","theme":["Earth Science"],"title":"Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM)"},"description":"SOCCOM (Southern Ocean Carbon and Climate Observations and Modeling project) is a NSF project sampling the Southern Ocean and its influence on climate.Additional Data LinksCLIVAR P16S_2014 Pigment AnalysisCLIVAR P16S_2014 POC dataCLIVAR P16S_2014 Supporting Documentation","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/067f97b5-c9b5-45f4-81a6-7cca93de1a0d","harvest_record_raw":"https://catalog.data.gov/harvest_record/067f97b5-c9b5-45f4-81a6-7cca93de1a0d/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SeaBASS/SOCCOM/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"last_harvested_date":"2026-09-23T00:13:45.557582","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS","slug":"southern-ocean-carbon-and-climate-observations-and-modeling-project-soccom","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM)","type":"dataset"},{"_score":18.869114,"_sort":[1790122417462,18.869114,2,"4882e8c0-4707-4b28-a5a2-9ab58391537a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The GPM Ground Validation CXSI Radar Imagery OLYMPEX dataset contains radar reflectivity and precipitation rate images obtained from Environment and Climate Change Canada (ECCC)\u2019s weather radar network during the GPM Ground Validation Olympic Mountain Experiment (OLYMPEX), which was conducted to validate rain and snow measurements in mid latitude frontal systems as they move from ocean to coast to mountains and to determine how remotely sensed measurements of precipitation by GPM can be applied to a range of hydrologic, weather forecasting, and climate data. These data are available as GIF images for November 19, 2015 through December 31, 2015.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1980116575-GHRC_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.5067/GPMGV/OLYMPEX/DATA101","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"http://dx.doi.org/10.7924/G8CC0XMR","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"http://olympex.atmos.washington.edu/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/JHM-D-16-0187.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.earthdata.nasa.gov/browseui/#pub/gpmcxsiolyx__1/docs/gpmcxsiolyx_dataset.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrc.nsstc.nasa.gov/home/field-campaigns/olympex","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1980116575-GHRC_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPMGV/OLYMPEX/CXSI/DATA101","keyword":["earth-science-precipitation-atmosphere-precipitation-rate","earth-science-radar-spectral-engineering-radar-reflectivity"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/MSFC/GHRC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -124.476, \"NorthBoundingCoordinate\": 49.3183, \"EastBoundingCoordinate\": -123.118, \"SouthBoundingCoordinate\": 48.4119}]]","temporal":"2015-11-10/2015-12-31","theme":["Earth Science"],"title":"GPM Ground Validation CXSI Radar Imagery OLYMPEX"},"description":"The GPM Ground Validation CXSI Radar Imagery OLYMPEX dataset contains radar reflectivity and precipitation rate images obtained from Environment and Climate Change Canada (ECCC)\u2019s weather radar network during the GPM Ground Validation Olympic Mountain Experiment (OLYMPEX), which was conducted to validate rain and snow measurements in mid latitude frontal systems as they move from ocean to coast to mountains and to determine how remotely sensed measurements of precipitation by GPM can be applied to a range of hydrologic, weather forecasting, and climate data. These data are available as GIF images for November 19, 2015 through December 31, 2015.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3a21a600-c432-4999-814f-1062b9195e40","harvest_record_raw":"https://catalog.data.gov/harvest_record/3a21a600-c432-4999-814f-1062b9195e40/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPMGV/OLYMPEX/CXSI/DATA101","keyword":["earth-science-precipitation-atmosphere-precipitation-rate","earth-science-radar-spectral-engineering-radar-reflectivity"],"last_harvested_date":"2026-09-23T00:13:37.462861","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"NASA/MSFC/GHRC","slug":"gpm-ground-validation-cxsi-radar-imagery-olympex","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM Ground Validation CXSI Radar Imagery OLYMPEX","type":"dataset"},{"_score":10.951335,"_sort":[1790122416006,10.951335,1,"012134cf-1f72-40ef-9e64-2bccb66e831b"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"TOLNet_UAH_Data is the lidar data collected by the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3880797903-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/outreach-material/introduction-to-tolnet-storymap","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/outreach-material/tolnet-stratospheric-intrusion-storymap","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/wagdocuments/473/TOLNet_Lidars_and_Corresponding_Campaigns.docx","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880797903-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/JTECH-D-10-05043.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/JTECH-D-10-05044.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1364/AO.41.007550","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/Lidar/Ozone/TOLNet/UAH","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5194/amt-10-3865-2017","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5194/amt-6-801-2013","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5194/amt-7-3529-2014","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://dx.doi.org/10.1364/AO.52.003557","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880797903-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://tolnet.larc.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/Lidar/Ozone/TOLNet/UAH","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species","earth-science-atmospheric-pressure-atmosphere","earth-science-atmospheric-temperature-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -88.15, \"EastBoundingCoordinate\": -86.6, \"SouthBoundingCoordinate\": 30.25, \"NorthBoundingCoordinate\": 42.505}]], Maximum Altitude, 15.2 km","temporal":"2009-06-29/2026-09-14","theme":["Earth Science"],"title":"TOLNet University of Alabama in Huntsville Data"},"description":"TOLNet_UAH_Data is the lidar data collected by the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.\r\n\r\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\r\n\r\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0af83127-2191-4763-ac3d-5e0da1a75419","harvest_record_raw":"https://catalog.data.gov/harvest_record/0af83127-2191-4763-ac3d-5e0da1a75419/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/Lidar/Ozone/TOLNet/UAH","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species","earth-science-atmospheric-pressure-atmosphere","earth-science-atmospheric-temperature-atmosphere"],"last_harvested_date":"2026-09-23T00:13:36.006109","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"tolnet-university-of-alabama-in-huntsville-data","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"TOLNet University of Alabama in Huntsville Data","type":"dataset"},{"_score":19.99377,"_sort":[1790122415613,19.99377,4,"5e150397-0419-4937-b312-7bcbcd7ef535"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"ARCTAS_Ozonesondes_Data contains data collected via ozonesonde launches during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3228971149-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://acp.copernicus.org/articles/10/5191/2010/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/MISR","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3228971149-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ASDC/SUBORBITAL/ARCTAS_Ozonesondes_Data_1","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://espo.nasa.gov/arctas","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/arctas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3228971149-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www-air.larc.nasa.gov/cgi-bin/ic2008r","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www-air.larc.nasa.gov/missions/arctas/docs/arctas_wp.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.5067/ASDC/SUBORBITAL/ARCTAS_Ozonesondes_Data_1","keyword":["earth-science-altitude-atmosphere","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-temperature-atmosphere","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-atmospheric-winds-atmosphere-upper-level-winds"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": 38.48, \"EastBoundingCoordinate\": 156.6, \"SouthBoundingCoordinate\": 39.95, \"NorthBoundingCoordinate\": 74.71}]], Maximum Altitude, 38.5 km","temporal":"2008-04-01/2008-07-12","theme":["Earth Science"],"title":"ARCTAS Ozonesondes Data"},"description":"ARCTAS_Ozonesondes_Data contains data collected via ozonesonde launches during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e927aa56-d423-4e10-b7aa-2406fbe6cbe5","harvest_record_raw":"https://catalog.data.gov/harvest_record/e927aa56-d423-4e10-b7aa-2406fbe6cbe5/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ASDC/SUBORBITAL/ARCTAS_Ozonesondes_Data_1","keyword":["earth-science-altitude-atmosphere","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-pressure-atmosphere-atmospheric-pressure-measurements","earth-science-atmospheric-temperature-atmosphere","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-temperature-atmosphere-upper-air-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-atmospheric-winds-atmosphere-upper-level-winds"],"last_harvested_date":"2026-09-23T00:13:35.613601","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":4,"publisher":"NASA/LARC/SD/ASDC","slug":"arctas-ozonesondes-data","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ARCTAS Ozonesondes Data","type":"dataset"},{"_score":19.761032,"_sort":[1790122415225,19.761032,1,"b1e81aba-aaa1-4fbe-9a63-a45d9a7a03b7"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"ARCTAS_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorological and navigational data for the P-3B aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C3228971222-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://acp.copernicus.org/articles/10/5191/2010/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/MISR","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3228971222-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ASDC/SUBORBITAL/ARCTAS_MetNav_AircraftInSitu_P3B_Data_1","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://espo.nasa.gov/arctas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/arctas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3228971222-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www-air.larc.nasa.gov/cgi-bin/ic2008r","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www-air.larc.nasa.gov/missions/arctas/docs/arctas_wp.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.5067/ASDC/SUBORBITAL/ARCTAS_MetNav_AircraftInSitu_P3B_Data_1","keyword":["earth-science-atmospheric-pressure-atmosphere-differential-pressure","earth-science-atmospheric-pressure-atmosphere-static-pressure","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-atmospheric-winds-atmosphere-upper-level-winds"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -163.6, \"EastBoundingCoordinate\": -68.69, \"SouthBoundingCoordinate\": 32.93, \"NorthBoundingCoordinate\": 80.67}]], Maximum Altitude, 9 km","temporal":"2008-03-25/2008-07-12","theme":["Earth Science"],"title":"ARCTAS P-3B Aircraft In-situ Meteorological and Navigational Data"},"description":"ARCTAS_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorological and navigational data for the P-3B aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data collection for this product is complete.\r\n\r\nThe Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA\u2019s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.\r\n\r\nARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.\r\n\r\nARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.\r\n\r\nDuring ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.\r\n\r\nThe ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth\u2019s environment and climate.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/33949052-de6f-493b-a64b-90600227c0a7","harvest_record_raw":"https://catalog.data.gov/harvest_record/33949052-de6f-493b-a64b-90600227c0a7/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ASDC/SUBORBITAL/ARCTAS_MetNav_AircraftInSitu_P3B_Data_1","keyword":["earth-science-atmospheric-pressure-atmosphere-differential-pressure","earth-science-atmospheric-pressure-atmosphere-static-pressure","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-atmospheric-winds-atmosphere-upper-level-winds"],"last_harvested_date":"2026-09-23T00:13:35.225978","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"arctas-p-3b-aircraft-in-situ-meteorological-and-navigational-data","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"ARCTAS P-3B Aircraft In-situ Meteorological and Navigational Data","type":"dataset"},{"_score":8.541273,"_sort":[1790122387476,8.541273,1,"b2fdd8f1-654a-4eb1-a4a5-d10059a9dac8"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.\n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each \"scene\" (instrument look) using the cloud information,\nand develop a merged, multi-sensor climatology of atmospheric water vapor as a\nfunction of altitude, stratified by the cloud classes. This is a large science\nanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find\nspace/time \"matchups\" between pairs of instruments, and process years of\nsatellite data to produce the climate data records.\n\nThe short name for this collection is AIRSM_CPR_MAT\n\nParameters contained in the data files include the following:\nVariable Name|Description|Units \n CH4_total_column|Retrieved total column CH4| (molecules/cm2)\n CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None)\n CloudLayers| Number of hydrometeor layers| (count)\n clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2)\n CO_total_column|Retrieved total column CO| (molecules/cm2)\n CPR_Cloud_mask| CPR Cloud Mask |(None)\n Data_quality| Data Quality |(None)\n H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg)\n H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air)\n MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None)\n MODIS_scene_var |MODIS scene variability| (None)\n nSurfStd|1-based index of the first valid level|(None)\n O3VMRStd|Ozone Volume Mixing Ratio|(vmr)\n olr|All-sky Outgoing Longwave Radiation|(Watts/m**2)\n Radar_Reflectivity| Radar Reflectivity Factor| (dBZe)\n Sigma-Zero| Sigma-Zero| (dB*100)\n TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K)\n TCldTopStd|Cloud top temperature|(K)\n totH2OStd|Total precipitable water vapor| (kg/m**2)\n totO3Std|Total ozone burden| (Dobson)\n TSurfAir|Atmospheric Temperature at Surface|(K)\n TSurfStd|Surface skin temperature|(K)\nEnd of parameter information","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1236224182-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/AIRSM_CPR_MAT_3.2.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRSM_CPR_MAT_3.2.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/Fetzer/README.AIRS_CloudSat.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/AIRS_CloudSat/AIRSM_CPR_MAT.3.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1236224182-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/about/competitive-programs/measures","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MEASURES/WVCC/DATA201","keyword":["earth-science-clouds-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2006-06-15/2012-12-14","theme":["Earth Science"],"title":"AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT)"},"description":"This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.\n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each \"scene\" (instrument look) using the cloud information,\nand develop a merged, multi-sensor climatology of atmospheric water vapor as a\nfunction of altitude, stratified by the cloud classes. This is a large science\nanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find\nspace/time \"matchups\" between pairs of instruments, and process years of\nsatellite data to produce the climate data records.\n\nThe short name for this collection is AIRSM_CPR_MAT\n\nParameters contained in the data files include the following:\nVariable Name|Description|Units \n CH4_total_column|Retrieved total column CH4| (molecules/cm2)\n CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None)\n CloudLayers| Number of hydrometeor layers| (count)\n clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2)\n CO_total_column|Retrieved total column CO| (molecules/cm2)\n CPR_Cloud_mask| CPR Cloud Mask |(None)\n Data_quality| Data Quality |(None)\n H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg)\n H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air)\n MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None)\n MODIS_scene_var |MODIS scene variability| (None)\n nSurfStd|1-based index of the first valid level|(None)\n O3VMRStd|Ozone Volume Mixing Ratio|(vmr)\n olr|All-sky Outgoing Longwave Radiation|(Watts/m**2)\n Radar_Reflectivity| Radar Reflectivity Factor| (dBZe)\n Sigma-Zero| Sigma-Zero| (dB*100)\n TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K)\n TCldTopStd|Cloud top temperature|(K)\n totH2OStd|Total precipitable water vapor| (kg/m**2)\n totO3Std|Total ozone burden| (Dobson)\n TSurfAir|Atmospheric Temperature at Surface|(K)\n TSurfStd|Surface skin temperature|(K)\nEnd of parameter information","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f981a6d3-6641-46ad-80fb-55268847d5f7","harvest_record_raw":"https://catalog.data.gov/harvest_record/f981a6d3-6641-46ad-80fb-55268847d5f7/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MEASURES/WVCC/DATA201","keyword":["earth-science-clouds-atmosphere"],"last_harvested_date":"2026-09-23T00:13:07.476959","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"airs-amsu-variables-cloudsat-cloud-mask-radar-reflectivities-and-cloud-classification-matc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT)","type":"dataset"},{"_score":10.068407,"_sort":[1790122386753,10.068407,1,"3607a31b-0442-4bf5-bf19-5a1a58b68824"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. \n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each \"scene\" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time \"matchups\" between pairs of instruments, and process years of satellite data to produce the climate data records.\n\nThe short name for this collection is AIRS_CPR_MAT\n\nParameters contained in the data files include the following:\nVariable Name|Description|Units \n CldFrcStdErr|Cloud Fraction|(None)\n CloudLayers| Number of hydrometeor layers| (count)\n CPR_Cloud_mask| CPR Cloud Mask| (None)\n DEM_elevation| Digital Elevation Map| (m)\n dust_flag|Dust Flag|(None)\n latAIRS|AIRS IR latitude|(deg)\n Latitude|CloudSat Latitude |(degrees)\n LayerBase| Height of Layer Base| (m)\n LayerTop| Height of layer top| (m)\n lonAIRS|AIRS IR longitude|(deg)\n Longitude|CloudSat Longitude| (degrees)\n MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None)\n Radar_Reflectivity| Radar Reflectivity Factor| (dBZe)\n radiances|Radiances|(milliWatts/m**2/cm**-1/steradian)\n Sigma-Zero| Sigma-Zero| (dB*100)\n spectral_clear_indicator|Spectral Clear Indicator|(None)\n Vertical_binsize|CloudSat vertical binsize| (m)\nEnd of parameter information","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1236224153-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/AIRS_CPR_MAT_3.2.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS_CPR_MAT_3.2.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/Fetzer/README.AIRS_CloudSat.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/AIRS_CloudSat/AIRS_CPR_MAT.3.2/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1236224153-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/about/competitive-programs/measures","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MEASURES/WVCC/DATA203","keyword":["earth-science-clouds-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2006-06-15/2012-12-14","theme":["Earth Science"],"title":"AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT)"},"description":"This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. \n\nThe basic task is to bring together retrievals of water vapor and cloud properties from multiple \"A-train\" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each \"scene\" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time \"matchups\" between pairs of instruments, and process years of satellite data to produce the climate data records.\n\nThe short name for this collection is AIRS_CPR_MAT\n\nParameters contained in the data files include the following:\nVariable Name|Description|Units \n CldFrcStdErr|Cloud Fraction|(None)\n CloudLayers| Number of hydrometeor layers| (count)\n CPR_Cloud_mask| CPR Cloud Mask| (None)\n DEM_elevation| Digital Elevation Map| (m)\n dust_flag|Dust Flag|(None)\n latAIRS|AIRS IR latitude|(deg)\n Latitude|CloudSat Latitude |(degrees)\n LayerBase| Height of Layer Base| (m)\n LayerTop| Height of layer top| (m)\n lonAIRS|AIRS IR longitude|(deg)\n Longitude|CloudSat Longitude| (degrees)\n MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None)\n Radar_Reflectivity| Radar Reflectivity Factor| (dBZe)\n radiances|Radiances|(milliWatts/m**2/cm**-1/steradian)\n Sigma-Zero| Sigma-Zero| (dB*100)\n spectral_clear_indicator|Spectral Clear Indicator|(None)\n Vertical_binsize|CloudSat vertical binsize| (m)\nEnd of parameter information","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/24d10c53-ef14-4d40-b866-3ee548d02f9c","harvest_record_raw":"https://catalog.data.gov/harvest_record/24d10c53-ef14-4d40-b866-3ee548d02f9c/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MEASURES/WVCC/DATA203","keyword":["earth-science-clouds-atmosphere"],"last_harvested_date":"2026-09-23T00:13:06.753550","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"airs-cloudsat-cloud-mask-radar-reflectivities-and-cloud-classification-matchups-v3-2-airs_","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT)","type":"dataset"},{"_score":45.762074,"_sort":[1790122386395,45.762074,1,"f2ff1ede-e306-4399-a597-6d4d5853cacf"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10\u00b0 N to 25\u00b0 S, 30\u00b0 to 85\u00b0 W. The data are in ASCII GRID format. \r\n\r\nThe data consist of the following: \r\n\r\nMonthly mean air temperature time series (1960-1990), in degrees C: \r\nmonthly mean air temperatures for 1960-1990 \r\ncross validation errors associated with time series \r\nmonthly mean air temperatures for 1960-1990, DEM assisted interpolation \r\ncross validation errors associated with DEM assisted interpolation time series \r\n\r\nMonthly mean air temperature climatology, in degrees C: \r\nclimatic means of monthly and annual air temperatures \r\ncross validation errors associated with climatic means \r\nclimatic means of monthly and annual mean air temperatures, DEM assisted interpolation \r\ncross validation errors associated with DEM assisted interpolation climatic means \r\n\r\nMonthly total precipitation time series (1960-1990), in millimeters: \r\nmonthly precipitation totals for 1960-1990 \r\ncross validation errors associated with time series \r\nmonthly precipitation totals for 1960-1990, climatologically aided interpolation \r\ncross validation errors associated with climatologically aided interpolation time series \r\n\r\nMonthly total precipitation climatology, in millimeters: \r\nclimatic means of monthly and annual precipitation totals \r\ncross validation errors associated with climatic means \r\nMore information about the full data set can be found at \"Willmott, Matsuura, and Collaborators' Global Climate Resource Pages\" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for \"Available Climate Data,\" register or sign in, and follow the link for \"South American Climate Data.\" \r\n\r\nInformation on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2779732234-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/project/square/lba_logo_square.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/willmott_673.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/physical_climate/willmott/comp/README","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/physical_climate/willmott/comp/willmott_readme.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/673","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2779732234-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/673","keyword":["earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-precipitation-atmosphere-precipitation-amount"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -85.0, \"NorthBoundingCoordinate\": 10.0, \"EastBoundingCoordinate\": -30.0, \"SouthBoundingCoordinate\": -25.0}]]","temporal":"1960-01-01/1990-12-31","theme":["Earth Science"],"title":"LBA Regional Climate Data, 0.5-Degree Grid, 1960-1990 (Willmott and Webber)"},"description":"This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10\u00b0 N to 25\u00b0 S, 30\u00b0 to 85\u00b0 W. The data are in ASCII GRID format. \r\n\r\nThe data consist of the following: \r\n\r\nMonthly mean air temperature time series (1960-1990), in degrees C: \r\nmonthly mean air temperatures for 1960-1990 \r\ncross validation errors associated with time series \r\nmonthly mean air temperatures for 1960-1990, DEM assisted interpolation \r\ncross validation errors associated with DEM assisted interpolation time series \r\n\r\nMonthly mean air temperature climatology, in degrees C: \r\nclimatic means of monthly and annual air temperatures \r\ncross validation errors associated with climatic means \r\nclimatic means of monthly and annual mean air temperatures, DEM assisted interpolation \r\ncross validation errors associated with DEM assisted interpolation climatic means \r\n\r\nMonthly total precipitation time series (1960-1990), in millimeters: \r\nmonthly precipitation totals for 1960-1990 \r\ncross validation errors associated with time series \r\nmonthly precipitation totals for 1960-1990, climatologically aided interpolation \r\ncross validation errors associated with climatologically aided interpolation time series \r\n\r\nMonthly total precipitation climatology, in millimeters: \r\nclimatic means of monthly and annual precipitation totals \r\ncross validation errors associated with climatic means \r\nMore information about the full data set can be found at \"Willmott, Matsuura, and Collaborators' Global Climate Resource Pages\" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for \"Available Climate Data,\" register or sign in, and follow the link for \"South American Climate Data.\" \r\n\r\nInformation on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9baa19fe-cf5e-41be-972b-1fddd75cd4bd","harvest_record_raw":"https://catalog.data.gov/harvest_record/9baa19fe-cf5e-41be-972b-1fddd75cd4bd/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/673","keyword":["earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-precipitation-atmosphere-precipitation-amount"],"last_harvested_date":"2026-09-23T00:13:06.395542","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"lba-regional-climate-data-0-5-degree-grid-1960-1990-willmott-and-webber","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"LBA Regional Climate Data, 0.5-Degree Grid, 1960-1990 (Willmott and Webber)","type":"dataset"},{"_score":15.4980345,"_sort":[1790122385658,15.4980345,2,"26ac6bc8-8dff-4572-b0fa-99f008f84ebe"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data.  Within this subset the oldest data date from 1832 and the most recent from 1990.The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which was to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including:The National Climatic Data Center's (NCDC's) World Weather Records,CAC's Climate Anomaly Monitoring System (CAMS),NCAR's World Monthly Surface Station Climatology,CIRES' (Eischeid/Diaz) Global precipitation data set,P. Jones' Temperature data base for the world, andS. Nicholson's African precipitation database. Quality Control of the GHCN V1 database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied.LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2777330730-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/project/square/lba_logo_square.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/lba_ghcn_702.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/physical_climate/lba_ghcn/comp/GHCN_V1_README_LBA.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/physical_climate/lba_ghcn/comp/invent.for","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/physical_climate/lba_ghcn/comp/invent.sas","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/physical_climate/lba_ghcn/comp/lba_inventory_readme.txt","format":"TXT","mediaType":"text/plain"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/702","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2777330730-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/702","keyword":["earth-science-atmospheric-pressure-atmosphere-sea-level-pressure","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-precipitation-atmosphere-precipitation-amount"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -85.0, \"NorthBoundingCoordinate\": 10.0, \"EastBoundingCoordinate\": -30.0, \"SouthBoundingCoordinate\": -25.0}]]","temporal":"1832-07-01/1990-12-31","theme":["Earth Science"],"title":"LBA Regional Global Historical Climatology Network, V. 1, 1832-1990"},"description":"This data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data.  Within this subset the oldest data date from 1832 and the most recent from 1990.The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which was to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including:The National Climatic Data Center's (NCDC's) World Weather Records,CAC's Climate Anomaly Monitoring System (CAMS),NCAR's World Monthly Surface Station Climatology,CIRES' (Eischeid/Diaz) Global precipitation data set,P. Jones' Temperature data base for the world, andS. Nicholson's African precipitation database. Quality Control of the GHCN V1 database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied.LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/49ed8d6e-c8cd-4d9f-a036-551ba1ec5555","harvest_record_raw":"https://catalog.data.gov/harvest_record/49ed8d6e-c8cd-4d9f-a036-551ba1ec5555/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/702","keyword":["earth-science-atmospheric-pressure-atmosphere-sea-level-pressure","earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-precipitation-atmosphere-precipitation-amount"],"last_harvested_date":"2026-09-23T00:13:05.658973","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"ORNL_DAAC","slug":"lba-regional-global-historical-climatology-network-v-1-1832-1990","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"LBA Regional Global Historical Climatology Network, V. 1, 1832-1990","type":"dataset"},{"_score":13.744064,"_sort":[1790122381592,13.744064,1,"53d61fe1-aec8-4f20-a96e-bf51d70fb2ee"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10\u00b0 N to 25\u00b0 S, 30\u00b0 to 85\u00b0 W).\r\n\r\nThe global river discharge data set (Coe and Olejniczak 1999), formerly known as the \"Climate, People, and Environment Program (CPEP) Global River Discharge Database,\" is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.\r\n\r\nThe purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.\r\n\r\nMore information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.\r\n\r\nLBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2777328159-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/project/square/lba_logo_square.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/sage_685.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/surf_hydro_and_water_chem/sage/comp/README","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/685","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2777328159-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/685","keyword":["earth-science-surface-water-terrestrial-hydrosphere-surface-water-features","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -85.0, \"NorthBoundingCoordinate\": 10.0, \"EastBoundingCoordinate\": -30.0, \"SouthBoundingCoordinate\": -25.0}]]","temporal":"1903-01-01/1999-12-31","theme":["Earth Science"],"title":"LBA Regional River Discharge Data (Coe and Olejniczak)"},"description":"This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10\u00b0 N to 25\u00b0 S, 30\u00b0 to 85\u00b0 W).\r\n\r\nThe global river discharge data set (Coe and Olejniczak 1999), formerly known as the \"Climate, People, and Environment Program (CPEP) Global River Discharge Database,\" is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.\r\n\r\nThe purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.\r\n\r\nMore information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.\r\n\r\nLBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8c1babb4-2f6c-4aca-bd16-d7e2f92cdb21","harvest_record_raw":"https://catalog.data.gov/harvest_record/8c1babb4-2f6c-4aca-bd16-d7e2f92cdb21/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/685","keyword":["earth-science-surface-water-terrestrial-hydrosphere-surface-water-features","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-23T00:13:01.592730","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"lba-regional-river-discharge-data-coe-and-olejniczak","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"LBA Regional River Discharge Data (Coe and Olejniczak)","type":"dataset"},{"_score":8.876841,"_sort":[1790122381193,8.876841,3,"1579b33f-ab25-41d4-b0c9-a6945455f93d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 1km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.Characterization of terrestrial vegetation from the Advanced Very High Resolution Radiometer (AVHRR) on the global to regional scale has traditionally been accomplished using classification schemes with discrete numbers of vegetation classes. Representation of vegetation into a limited number of homogeneous classes does not account for the variability within land cover, nor does the portrayal recognize transition zones between adjacent cover types. An alternative paradigm to describing land cover as discrete classes is to represent land cover as continuous fields of vegetation characteristics using a linear mixture model approach. This prototype data set, created by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland, contains 1-km cells estimating: 1) Percent tree cover; 2) Percentage cover for two layers representing leaf longevity (evergreen and deciduous); and 3) Percentage cover for two layers estimating leaf type (broadleaf and needleleaf).Data acquired in 1992-93 from NOAA's AVHRR at a 1-km spatial resolution and processed under the guidance of the International Geosphere Biosphere Programme (IGBP) were used to derive the tree cover, leaf type and leaf longevity maps.  Each pixel in the layers has a value between 10 and 80 percent. These layers can be directly used as parameters in models or aggregated into more conventional land cover maps. For the latter, the product offers the flexibility to derive land cover maps based on user's requirements for a particular application. The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial data sets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2804819697-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/686_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/lba_tree_cover-1km_686.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/land_use_land_cover_change/lba_tree_cover-1km/comp/glcftree_readme.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/686","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2804819697-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/686","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-vegetation-biosphere-deciduous-vegetation","earth-science-vegetation-biosphere-evergreen-vegetation","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-vegetation-cover"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -85.0, \"NorthBoundingCoordinate\": 10.0, \"EastBoundingCoordinate\": -30.0, \"SouthBoundingCoordinate\": -25.0}]]","temporal":"1992-01-01/1993-12-31","theme":["Earth Science"],"title":"LBA Regional Tree Cover from AVHRR, 1-km, 1992-1993 (DeFries et al.)"},"description":"The data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 1km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.Characterization of terrestrial vegetation from the Advanced Very High Resolution Radiometer (AVHRR) on the global to regional scale has traditionally been accomplished using classification schemes with discrete numbers of vegetation classes. Representation of vegetation into a limited number of homogeneous classes does not account for the variability within land cover, nor does the portrayal recognize transition zones between adjacent cover types. An alternative paradigm to describing land cover as discrete classes is to represent land cover as continuous fields of vegetation characteristics using a linear mixture model approach. This prototype data set, created by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland, contains 1-km cells estimating: 1) Percent tree cover; 2) Percentage cover for two layers representing leaf longevity (evergreen and deciduous); and 3) Percentage cover for two layers estimating leaf type (broadleaf and needleleaf).Data acquired in 1992-93 from NOAA's AVHRR at a 1-km spatial resolution and processed under the guidance of the International Geosphere Biosphere Programme (IGBP) were used to derive the tree cover, leaf type and leaf longevity maps.  Each pixel in the layers has a value between 10 and 80 percent. These layers can be directly used as parameters in models or aggregated into more conventional land cover maps. For the latter, the product offers the flexibility to derive land cover maps based on user's requirements for a particular application. The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial data sets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/99ec161d-e962-48b4-b862-f3b127fc5c2e","harvest_record_raw":"https://catalog.data.gov/harvest_record/99ec161d-e962-48b4-b862-f3b127fc5c2e/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/686","keyword":["earth-science-land-use-land-cover-land-surface-land-use-land-cover-classification","earth-science-vegetation-biosphere-deciduous-vegetation","earth-science-vegetation-biosphere-evergreen-vegetation","earth-science-vegetation-biosphere-leaf-characteristics","earth-science-vegetation-biosphere-vegetation-cover"],"last_harvested_date":"2026-09-23T00:13:01.193904","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"ORNL_DAAC","slug":"lba-regional-tree-cover-from-avhrr-1-km-1992-1993-defries-et-al","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"LBA Regional Tree Cover from AVHRR, 1-km, 1992-1993 (DeFries et al.)","type":"dataset"},{"_score":13.744197,"_sort":[1790122350163,13.744197,1,"94bad365-7d53-423d-88bd-1ebd5059a4a2"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The provided data were generated by the Integrated BIosphere Simulator (IBIS) terrestrial ecosystem model (Foley et al. 1996, Kucharik et al. 2000) using data from the CRU05 climate record for the years 1921-1998 (New et al. 2000). Data are included for the annual net ecosystem exchange of the surface, microbial respiration, root respiration, total soil respiration, soil moisture, leaf area index, drainage, and surface and subsurface runoff, for the entire Amazon and Tocantins basins. \r\n\r\nThe data files are provided in netCDF format and standard ESRI ARCGIS ARC/INFO ASCIIGRID format. The netCDF files consist of either annual or monthly means from 1921 to 1998. The ASCII files are available only for the annual mean files.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2777289829-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/project/square/lba_logo_square.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/LC04_IBIS_Model_1139.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/land_use_land_cover_change/LC04_IBIS_Model/comp/LC04_IBIS_Model.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/1139","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2777289829-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/1139","keyword":["earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-ecological-dynamics-biosphere-ecosystem-functions","earth-science-ecological-dynamics-biosphere-fire-ecology"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -80.0, \"NorthBoundingCoordinate\": 6.0, \"EastBoundingCoordinate\": -45.0, \"SouthBoundingCoordinate\": -21.0}]]","temporal":"1921-01-01/1998-12-31","theme":["Earth Science"],"title":"LBA-ECO LC-04 IBIS Model Simulations for the Amazon and Tocantins Basins: 1921-1998"},"description":"The provided data were generated by the Integrated BIosphere Simulator (IBIS) terrestrial ecosystem model (Foley et al. 1996, Kucharik et al. 2000) using data from the CRU05 climate record for the years 1921-1998 (New et al. 2000). Data are included for the annual net ecosystem exchange of the surface, microbial respiration, root respiration, total soil respiration, soil moisture, leaf area index, drainage, and surface and subsurface runoff, for the entire Amazon and Tocantins basins. \r\n\r\nThe data files are provided in netCDF format and standard ESRI ARCGIS ARC/INFO ASCIIGRID format. The netCDF files consist of either annual or monthly means from 1921 to 1998. The ASCII files are available only for the annual mean files.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f2fb8714-1c7e-4e74-8cec-53dc53fb5fb8","harvest_record_raw":"https://catalog.data.gov/harvest_record/f2fb8714-1c7e-4e74-8cec-53dc53fb5fb8/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/1139","keyword":["earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds","earth-science-ecological-dynamics-biosphere-ecosystem-functions","earth-science-ecological-dynamics-biosphere-fire-ecology"],"last_harvested_date":"2026-09-23T00:12:30.163415","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"ORNL_DAAC","slug":"lba-eco-lc-04-ibis-model-simulations-for-the-amazon-and-tocantins-basins-1921-1998","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"LBA-ECO LC-04 IBIS Model Simulations for the Amazon and Tocantins Basins: 1921-1998","type":"dataset"},{"_score":42.583916,"_sort":[1790122348724,42.583916,1,"c5479d44-3c3a-4f53-81f8-a50981d1fb2d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Measurements made in the Gulf of Guinea off of west-central Africa in 2006 as part of the third cruise in the EGEE project (Gulf of Guinea climate and ocean circulation study, which is the oceanographic strand of the AMMA -African Monsoon Multidisciplinary Analyses program).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633360226-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://seabass.gsfc.nasa.gov/experiment/EGEE3/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633360226-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SeaBASS/EGEE3/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180.0, \"NorthBoundingCoordinate\": 90.0, \"SouthBoundingCoordinate\": -90.0, \"WestBoundingCoordinate\": -180.0}]]","temporal":"2006-05-29/2026-09-14","theme":["Earth Science"],"title":"Gulf of Guinea climate and ocean circulation study (EGEE) project - Gulf of Guinea off of west-central Africa"},"description":"Measurements made in the Gulf of Guinea off of west-central Africa in 2006 as part of the third cruise in the EGEE project (Gulf of Guinea climate and ocean circulation study, which is the oceanographic strand of the AMMA -African Monsoon Multidisciplinary Analyses program).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3ca3658d-7dbf-4fd6-a438-26e41a5dc0d0","harvest_record_raw":"https://catalog.data.gov/harvest_record/3ca3658d-7dbf-4fd6-a438-26e41a5dc0d0/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SeaBASS/EGEE3/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"last_harvested_date":"2026-09-23T00:12:28.724236","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS","slug":"gulf-of-guinea-climate-and-ocean-circulation-study-egee-project-gulf-of-guinea-off-of-west","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Gulf of Guinea climate and ocean circulation study (EGEE) project - Gulf of Guinea off of west-central Africa","type":"dataset"},{"_score":49.884373,"_sort":[1790122348018,49.884373,1,"20c3fe6a-1539-4f3e-87ee-8c4434039951"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Measurements made in the Gulf of Guinea off of west-central Africa in 2007 as part of the fifth cruise in the EGEE project (Gulf of Guinea climate and ocean circulation study, which is the oceanographic strand of the AMMA -African Monsoon Multidisciplinary Analyses program).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633360227-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://seabass.gsfc.nasa.gov/experiment/EGEE5/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633360227-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SeaBASS/EGEE5/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180.0, \"NorthBoundingCoordinate\": 90.0, \"SouthBoundingCoordinate\": -90.0, \"WestBoundingCoordinate\": -180.0}]]","temporal":"2007-06-06/2026-09-14","theme":["Earth Science"],"title":"Gulf of Guinea climate and ocean circulation study (EGEES)"},"description":"Measurements made in the Gulf of Guinea off of west-central Africa in 2007 as part of the fifth cruise in the EGEE project (Gulf of Guinea climate and ocean circulation study, which is the oceanographic strand of the AMMA -African Monsoon Multidisciplinary Analyses program).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5f6c317d-07d5-4ade-96cf-58a76c5690e8","harvest_record_raw":"https://catalog.data.gov/harvest_record/5f6c317d-07d5-4ade-96cf-58a76c5690e8/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SeaBASS/EGEE5/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"last_harvested_date":"2026-09-23T00:12:28.018117","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS","slug":"gulf-of-guinea-climate-and-ocean-circulation-study-egees","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Gulf of Guinea climate and ocean circulation study (EGEES)","type":"dataset"},{"_score":12.585151,"_sort":[1790122344705,12.585151,3,"d86a4983-8458-433d-aada-63a96c73eaf2"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This data set provides Ecosystem Demography Model (ED) estimates of potential above-ground net primary production (NPP) (kg C/m2/y), potential average live biomass (kg C/m2), and potential average soil carbon (kg C/m2) for the Brazilian Amazon at 1 degree resolution. \r\n\r\nEcosystem Demography Model predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP, and evapotranspiration) from climate, soil, and land-use inputs. Estimates for the Brazilian Amazon include the effects of natural disturbances such as windthrow and fire, but do not include the effects of human land use. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988 and averaged into a single average year (Moorcroft et al., 2001).\r\n\r\nThe data are provided for the three estimates in both ASCII text and in NetCDF formatted files.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2784856942-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/graphics/browse/sdat-tds/1102_1_fit.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/protected/bundle/LC08_Ecosystem_Demography_Model_1102.zip","format":"ZIP","mediaType":"application/zip"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/lba/land_use_land_cover_change/LC08_Ecosystem_Demography_Model/comp/LC08_Ecosystem_Dynamics_model.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/1102","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2784856942-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/1102","keyword":["earth-science-ecological-dynamics-biosphere-ecosystem-functions","earth-science-vegetation-biosphere-biomass","earth-science-vegetation-biosphere-carbon"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -85.0, \"NorthBoundingCoordinate\": 15.0, \"EastBoundingCoordinate\": -30.0, \"SouthBoundingCoordinate\": -15.0}]]","temporal":"1987-01-01/1988-12-31","theme":["Earth Science"],"title":"LBA-ECO LC-08 Ecosystem Demography Model Estimated C, NPP, and Biomass For Amazonia"},"description":"This data set provides Ecosystem Demography Model (ED) estimates of potential above-ground net primary production (NPP) (kg C/m2/y), potential average live biomass (kg C/m2), and potential average soil carbon (kg C/m2) for the Brazilian Amazon at 1 degree resolution. \r\n\r\nEcosystem Demography Model predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP, and evapotranspiration) from climate, soil, and land-use inputs. Estimates for the Brazilian Amazon include the effects of natural disturbances such as windthrow and fire, but do not include the effects of human land use. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988 and averaged into a single average year (Moorcroft et al., 2001).\r\n\r\nThe data are provided for the three estimates in both ASCII text and in NetCDF formatted files.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/82709187-b40b-415e-926a-d278ba7f0812","harvest_record_raw":"https://catalog.data.gov/harvest_record/82709187-b40b-415e-926a-d278ba7f0812/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/1102","keyword":["earth-science-ecological-dynamics-biosphere-ecosystem-functions","earth-science-vegetation-biosphere-biomass","earth-science-vegetation-biosphere-carbon"],"last_harvested_date":"2026-09-23T00:12:24.705160","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"ORNL_DAAC","slug":"lba-eco-lc-08-ecosystem-demography-model-estimated-c-npp-and-biomass-for-amazonia","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"LBA-ECO LC-08 Ecosystem Demography Model Estimated C, NPP, and Biomass For Amazonia","type":"dataset"},{"_score":48.477814,"_sort":[1790122343275,48.477814,2,"751cec50-5adb-4c84-b6cf-498de36b7a6a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE) was a multi-year NASA shipborne project. The bulk of the research took place in the Beaufort and Chukchi Seas in the summers of 2010 and 2011.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633360375-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://seabass.gsfc.nasa.gov/experiment/ICESCAPE/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633360375-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SeaBASS/ICESCAPE/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180.0, \"NorthBoundingCoordinate\": 90.0, \"SouthBoundingCoordinate\": -90.0, \"WestBoundingCoordinate\": -180.0}]]","temporal":"2010-06-16/2026-09-14","theme":["Earth Science"],"title":"Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE)"},"description":"Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE) was a multi-year NASA shipborne project. The bulk of the research took place in the Beaufort and Chukchi Seas in the summers of 2010 and 2011.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/48a7ec9c-5342-40a3-822b-ab8065350aad","harvest_record_raw":"https://catalog.data.gov/harvest_record/48a7ec9c-5342-40a3-822b-ab8065350aad/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SeaBASS/ICESCAPE/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"last_harvested_date":"2026-09-23T00:12:23.275459","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS","slug":"impacts-of-climate-on-the-eco-systems-and-chemistry-of-the-arctic-pacific-environment-ices","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE)","type":"dataset"},{"_score":42.465908,"_sort":[1790122342921,42.465908,1,"c3aeac44-f5ec-4be2-818c-079e8ace548a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Title:  The Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)Research Team:* Antonio Mannino (PI) -  NASA GSFC* Michael Novak - NASA GSFC* Margaret Mulholland (co-PI) - Old Dominion University* Peter Bernhardt - Old Dominion University* CJ Staryk - Old Dominion University* Kimberly Hyde (co-PI) - NOAA NEFSC* Jon Hare (collaborator) - NOAA NEFSC* David Lary (co-I) - University of Texas at DallasObservations from the MODIS and SeaWiFS time series (1997-2012) and measurements from an extensive field campaign are employed to examine how inter-annual and decadal-scale climate variability affects primary productivity and organic carbon distributions along the continental margin of the U.S. northeast coast. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL vertically resolves phytoplankton absorption of photosynthetically active radiation (PAR) and relates the chlorophyll-specific absorption coefficient to sea-surface temperature (SST), where SST is a proxy for seasonal changes in the phytoplankton community. OPAL will be validated with new field measurements of PP including dissolved organic carbon production.Field measurements of particulate (POC) and dissolved organic carbon (DOC) and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, aph and aCDOM. Furthermore, we will apply our extensive field data to derive region-independent ocean color algorithms for PP, POC, DOC aCDOM and aph using machine learning approaches. We will rigorously validate and compare band-ratio and multivariate machine learning algorithms. Algorithms validated from this study will be applied to satellite observations to produce a time  series of satellite data productsThe U.S. Middle Atlantic Bight (MAB), George's Bank (GB) and Gulf of Maine (GoM) stand at the crossroads between major ocean circulation features - the Gulf Stream and Labrador slope-sea and shelf currents - and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Our work will focus on the impacts of variable river discharge, SST and large-scale climate indices on primary production, and POC and DOC distributions. These processes are not unique to the MAB and GoM. Consequently, the results from this activity can be applied to understanding how inter-annual and long-term variability in climate patterns can impact the carbon cycle of continental margins throughout the globe.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1633360191-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://seabass.gsfc.nasa.gov/experiment/CliVEC/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1633360191-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/SeaBASS/CLIVEC/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180.0, \"NorthBoundingCoordinate\": 90.0, \"SouthBoundingCoordinate\": -90.0, \"WestBoundingCoordinate\": -180.0}]]","temporal":"2009-08-17/2026-09-14","theme":["Earth Science"],"title":"Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)"},"description":"Title:  The Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)Research Team:* Antonio Mannino (PI) -  NASA GSFC* Michael Novak - NASA GSFC* Margaret Mulholland (co-PI) - Old Dominion University* Peter Bernhardt - Old Dominion University* CJ Staryk - Old Dominion University* Kimberly Hyde (co-PI) - NOAA NEFSC* Jon Hare (collaborator) - NOAA NEFSC* David Lary (co-I) - University of Texas at DallasObservations from the MODIS and SeaWiFS time series (1997-2012) and measurements from an extensive field campaign are employed to examine how inter-annual and decadal-scale climate variability affects primary productivity and organic carbon distributions along the continental margin of the U.S. northeast coast. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL vertically resolves phytoplankton absorption of photosynthetically active radiation (PAR) and relates the chlorophyll-specific absorption coefficient to sea-surface temperature (SST), where SST is a proxy for seasonal changes in the phytoplankton community. OPAL will be validated with new field measurements of PP including dissolved organic carbon production.Field measurements of particulate (POC) and dissolved organic carbon (DOC) and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, aph and aCDOM. Furthermore, we will apply our extensive field data to derive region-independent ocean color algorithms for PP, POC, DOC aCDOM and aph using machine learning approaches. We will rigorously validate and compare band-ratio and multivariate machine learning algorithms. Algorithms validated from this study will be applied to satellite observations to produce a time  series of satellite data productsThe U.S. Middle Atlantic Bight (MAB), George's Bank (GB) and Gulf of Maine (GoM) stand at the crossroads between major ocean circulation features - the Gulf Stream and Labrador slope-sea and shelf currents - and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Our work will focus on the impacts of variable river discharge, SST and large-scale climate indices on primary production, and POC and DOC distributions. These processes are not unique to the MAB and GoM. Consequently, the results from this activity can be applied to understanding how inter-annual and long-term variability in climate patterns can impact the carbon cycle of continental margins throughout the globe.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/30424076-1acb-4af5-b3b4-8678849f2166","harvest_record_raw":"https://catalog.data.gov/harvest_record/30424076-1acb-4af5-b3b4-8678849f2166/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/SeaBASS/CLIVEC/DATA001","keyword":["earth-science-ocean-chemistry-oceans","earth-science-ocean-optics-oceans","earth-science-ocean-temperature-oceans","earth-science-salinity-density-oceans"],"last_harvested_date":"2026-09-23T00:12:22.921151","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;NASA/GSFC/SED/ESD/GCDC/SeaBASS","slug":"impacts-of-climate-variability-on-primary-productivity-and-carbon-distributions-in-the-mid","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)","type":"dataset"},{"_score":10.940724,"_sort":[1790122321438,10.940724,2,"41c2dc56-37bd-40e7-a95d-a4104667f072"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C) \n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask) \n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag) \n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- sstref \u2014 Sea Surface Temperature reference (\u00b0C) \n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1658475742-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-2/SNPP-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1658475742-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/VIIRS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Suomi-NPP","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1658475742-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180, \"WestBoundingCoordinate\": -180, \"SouthBoundingCoordinate\": -90, \"NorthBoundingCoordinate\": 90}]]","temporal":"2012-01-02/2026-09-14","theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-2 Regional  11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version 2016.2"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C) \n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask) \n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag) \n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- sstref \u2014 Sea Surface Temperature reference (\u00b0C) \n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bf7c6fb3-8ead-462c-b8d9-062f180b9a9d","harvest_record_raw":"https://catalog.data.gov/harvest_record/bf7c6fb3-8ead-462c-b8d9-062f180b9a9d/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1658475742-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:12:01.438632","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"suomi-npp-viirs-level-2-regional-11m-day-night-sea-surface-temperature-sst-near-real-time--3f2e6","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-2 Regional  11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version 2016.2","type":"dataset"},{"_score":12.586679,"_sort":[1790122320364,12.586679,1,"4d220a1c-05c4-4215-a993-a462126d6cd5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1658475758-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Binned/SNPP-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1658475758-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/VIIRS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Suomi-NPP","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1658475758-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"SouthBoundingCoordinate\": -90, \"EastBoundingCoordinate\": 180, \"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180}]]","temporal":"2012-01-02/2026-09-14","theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Binned 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version 2016.2"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7ea8a8d0-81db-4c77-929e-9c3aac3656a0","harvest_record_raw":"https://catalog.data.gov/harvest_record/7ea8a8d0-81db-4c77-929e-9c3aac3656a0/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1658475758-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:12:00.364217","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"suomi-npp-viirs-level-3-global-binned-11m-day-night-sea-surface-temperature-sst-near-real--6caa7","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Binned 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version 2016.2","type":"dataset"},{"_score":13.942863,"_sort":[1790122319979,13.942863,2,"d13a1afa-6841-4ed7-87cb-2d3f7aa3cac1"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"NSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C) \n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1658475749-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Binned/SNPP-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1658475749-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/VIIRS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Suomi-NPP","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1658475749-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90, \"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180}]]","temporal":"2012-01-02/2026-09-14","theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Binned 11\u00b5m Nighttime Sea Surface Temperature (NSST) Data, version 2016.2"},"description":"NSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C) \n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/db9100be-f21c-48d3-b9fa-b2f946101aac","harvest_record_raw":"https://catalog.data.gov/harvest_record/db9100be-f21c-48d3-b9fa-b2f946101aac/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1658475749-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:11:59.979248","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"suomi-npp-viirs-level-3-global-binned-11m-nighttime-sea-surface-temperature-nsst-data-vers-5032e","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Binned 11\u00b5m Nighttime Sea Surface Temperature (NSST) Data, version 2016.2","type":"dataset"},{"_score":12.24415,"_sort":[1790122319623,12.24415,1,"7dc26a9d-c72e-4d30-9d08-aea5b26ed77f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nNSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C)\n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1658475747-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Binned/SNPP-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1658475747-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/VIIRS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Suomi-NPP","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1658475747-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"SouthBoundingCoordinate\": -90, \"EastBoundingCoordinate\": 180}]]","temporal":"2012-01-02/2026-09-14","theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Binned 11\u00b5m Nighttime Sea Surface Temperature NSST) - Near Real-time (NRT) Data, version 2016.2"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nNSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C)\n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c4a3fe14-b8cf-44bb-9b80-31a2b20f407f","harvest_record_raw":"https://catalog.data.gov/harvest_record/c4a3fe14-b8cf-44bb-9b80-31a2b20f407f/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1658475747-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:11:59.623380","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"suomi-npp-viirs-level-3-global-binned-11m-nighttime-sea-surface-temperature-nsst-near-real-67568","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Binned 11\u00b5m Nighttime Sea Surface Temperature NSST) - Near Real-time (NRT) Data, version 2016.2","type":"dataset"},{"_score":14.38573,"_sort":[1790122318466,14.38573,1,"a7fc65e2-f071-441b-af9c-a21e795e8c8e"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"SST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C)\n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1658475777-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Mapped/SNPP-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/opendap/VIIRS/L3SMI/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1658475777-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/VIIRS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Suomi-NPP","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1658475777-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"SouthBoundingCoordinate\": -90, \"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180}]]","temporal":"2012-01-02/2026-09-14","theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Mapped 11\u00b5m Daytime Sea Surface Temperature (SST) Data, version 2016.2"},"description":"SST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- sst \u2014 Sea Surface Temperature (\u00b0C)\n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8991c9e9-7f25-4f89-a84b-28382eaa99d8","harvest_record_raw":"https://catalog.data.gov/harvest_record/8991c9e9-7f25-4f89-a84b-28382eaa99d8/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1658475777-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:11:58.466822","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"suomi-npp-viirs-level-3-global-mapped-11m-daytime-sea-surface-temperature-sst-data-version-9f934","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Mapped 11\u00b5m Daytime Sea Surface Temperature (SST) Data, version 2016.2","type":"dataset"},{"_score":13.944383,"_sort":[1790122318119,13.944383,2,"0c7fa074-bf12-4e25-b9d2-abe6ffe8332d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"NSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C) \n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1658475765-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Mapped/SNPP-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/opendap/VIIRS/L3SMI/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1658475765-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/VIIRS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Suomi-NPP","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1658475765-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"SouthBoundingCoordinate\": -90, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"WestBoundingCoordinate\": -180}]]","temporal":"2012-01-02/2026-09-14","theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Mapped 11\u00b5m Nighttime Sea Surface Temperature (NSST) Data, version 2016.2"},"description":"NSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C) \n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d6e31a4e-0b01-420e-ade4-2ca08f9422ff","harvest_record_raw":"https://catalog.data.gov/harvest_record/d6e31a4e-0b01-420e-ade4-2ca08f9422ff/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1658475765-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:11:58.119789","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"suomi-npp-viirs-level-3-global-mapped-11m-nighttime-sea-surface-temperature-nsst-data-vers-13823","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Mapped 11\u00b5m Nighttime Sea Surface Temperature (NSST) Data, version 2016.2","type":"dataset"},{"_score":12.204795,"_sort":[1790122317770,12.204795,1,"4247ca9f-22d0-4002-9a44-9d52efb80c18"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nNSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C)\n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1658475763-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Mapped/SNPP-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/opendap/VIIRS/L3SMI/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1658475763-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/VIIRS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Suomi-NPP","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1658475763-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"SouthBoundingCoordinate\": -90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180}]]","temporal":"2012-01-02/2026-09-14","theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Mapped 11\u00b5m Nighttime Sea Surface Temperature NSST) - Near Real-time (NRT) Data, version 2016.2"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nNSST provides global nighttime sea surface temperature estimates derived from thermal infrared observations acquired during local night to minimize diurnal warming and sun glint. Nighttime SST is widely used to detect ocean fronts, support fisheries habitat models (when paired with chlor_a, Kd_490, PAR), and provide stable inputs for air\u2013sea fluxes and climate analyses. As with all IR-based SST, uncertainties increase under thin clouds, heavy aerosols/dust, or strong thermal gradients\u2014refer to per-file flags and documentation before analysis.\n\n Geophysical variables in this suite include:\n  - sst \u2014 Sea Surface Temperature (\u00b0C)\n  - qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b5b89d4f-3513-4c81-a91d-828d1ce8df1d","harvest_record_raw":"https://catalog.data.gov/harvest_record/b5b89d4f-3513-4c81-a91d-828d1ce8df1d/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1658475763-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:11:57.770235","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"suomi-npp-viirs-level-3-global-mapped-11m-nighttime-sea-surface-temperature-nsst-near-real-d9c67","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Suomi-NPP VIIRS Level-3 Global Mapped 11\u00b5m Nighttime Sea Surface Temperature NSST) - Near Real-time (NRT) Data, version 2016.2","type":"dataset"},{"_score":11.051091,"_sort":[1790122315243,11.051091,1,"c74f5947-b694-4131-807b-37bd1b129f10"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C) \n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask) \n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag) \n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- sstref \u2014 Sea Surface Temperature reference (\u00b0C) \n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1641917076-OB_DAAC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://nasa.github.io/oceandata-notebooks/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-2/Terra-MODIS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1641917076-OB_DAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/instruments/MODIS","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/Terra","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1641917076-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"SouthBoundingCoordinate\": -90, \"EastBoundingCoordinate\": 180, \"WestBoundingCoordinate\": -180}]]","temporal":"2000-02-24/2026-09-14","theme":["Earth Science"],"title":"Terra MODIS Level-2 Regional  11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0"},"description":"The Ocean Biology DAAC produces near real-time (NRT) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Near real-time products provide a snapshot of the data during a short time period within a single orbit. \n\nSST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. Users apply SST to detect fronts and eddies, monitor marine heatwaves, drive ecosystem and habitat models (when paired with chlor_a, Kd_490, PAR), and provide boundary conditions for biogeochemical and climate analyses. Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst \u2014 Sea Surface Temperature bias (\u00b0C) \n- flags_sst \u2014 Product-specific flags, Sea Surface Temperature (integer bitmask) \n- l2_flags \u2014 Level-2 Processing Flags (integer bitmask; see bit definitions) \n- qual_sst \u2014 Quality levels for Sea Surface Temperature (integer quality flag) \n- sst \u2014 Sea Surface Temperature (\u00b0C) \n- sstref \u2014 Sea Surface Temperature reference (\u00b0C) \n- stdv_sst \u2014 Sea Surface Temperature standard deviation (\u00b0C)","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d1ec834f-d38d-45de-b333-445d57b6526d","harvest_record_raw":"https://catalog.data.gov/harvest_record/d1ec834f-d38d-45de-b333-445d57b6526d/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1641917076-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-23T00:11:55.243024","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG","slug":"terra-modis-level-2-regional-11m-day-night-sea-surface-temperature-sst-near-real-time-nrt--9039d","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Terra MODIS Level-2 Regional  11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version R2019.0","type":"dataset"}],"sort":"last_harvested_date"}