{"after":"WzE3OTEzMzI0MTA5NDQsMTEuMzg4OTA1LDIsIjQxYzJkYzU2LTM3YmQtNDBlNy1hOTVkLWE0MTA0NjY3ZjA3MiJd","results":[{"_score":32.583214,"_sort":[1791435534636,32.583214,7,"45da5901-9cf1-45be-95c2-78d11ddb5efe"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Matt Stumbaugh","hasEmail":"mailto:mrstu@uw.edu"},"description":"This archive contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes for the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.","distribution":[{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6776f3d3d34ee88ba0b15863.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f3d3d34ee88ba0b15863","keyword":["Pacific Northwest","USGS:6776f3d3d34ee88ba0b15863","Western United States","climate change","climate impacts","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","statistical downscaling","western United States"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.6500, 38.0900, -105.9600, 52.4000","theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data"},"description":"This archive contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes for the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/83a1adb5-7261-4cd3-a1a7-c533beb7231e","harvest_record_raw":"https://catalog.data.gov/harvest_record/83a1adb5-7261-4cd3-a1a7-c533beb7231e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f3d3d34ee88ba0b15863","keyword":["Pacific Northwest","USGS:6776f3d3d34ee88ba0b15863","Western United States","climate change","climate impacts","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","statistical downscaling","western United States"],"last_harvested_date":"2026-10-08T04:58:54.636004","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":7,"publisher":"U.S. Geological Survey","slug":"western-us-hydroclimate-scenarios-project-dynamically-downscaled-data","spatial_centroid":{"lat":43.814,"lon":-117.174},"spatial_shape":{"coordinates":[[[-124.65,38.09],[-124.65,52.4],[-105.96,52.4],[-105.96,38.09],[-124.65,38.09]]],"type":"Polygon"},"theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data","type":"dataset"},{"_score":32.64369,"_sort":[1791435266611,32.64369,0,"76a59cf2-7564-45b1-bd99-62fc3a0ac451"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Matt Stumbaugh","hasEmail":"mailto:mrstu@uw.edu"},"description":"This archive contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Note that time-stamps on these data are not in the future. This research was sponsored by a grant from the Department of the Interior, USGS NW Climate Science Center, a multi-institution DOI-funded project located at the University of Washington, Oregon State University, and the University of Idaho. We also acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.","distribution":[{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6776f4dbd34ee88ba0b15889.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f4dbd34ee88ba0b15889","keyword":["Atmosphere","Meteorology","Pacific Northwest","USGS:6776f4dbd34ee88ba0b15889","Western United States","climate change","climate impacts","climatology","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","service","statistical downscaling"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.6500, 31.3400, -103.0300, 52.8400","theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data"},"description":"This archive contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Note that time-stamps on these data are not in the future. This research was sponsored by a grant from the Department of the Interior, USGS NW Climate Science Center, a multi-institution DOI-funded project located at the University of Washington, Oregon State University, and the University of Idaho. We also acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/42e69266-18e0-4577-aa04-14e521d3cef9","harvest_record_raw":"https://catalog.data.gov/harvest_record/42e69266-18e0-4577-aa04-14e521d3cef9/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f4dbd34ee88ba0b15889","keyword":["Atmosphere","Meteorology","Pacific Northwest","USGS:6776f4dbd34ee88ba0b15889","Western United States","climate change","climate impacts","climatology","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","service","statistical downscaling"],"last_harvested_date":"2026-10-08T04:54:26.611536","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"western-us-hydroclimate-scenarios-project-observations-and-statistically-downscaled-data","spatial_centroid":{"lat":39.940000000000005,"lon":-116.002},"spatial_shape":{"coordinates":[[[-124.65,31.34],[-124.65,52.84],[-103.03,52.84],[-103.03,31.34],[-124.65,31.34]]],"type":"Polygon"},"theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data","type":"dataset"},{"_score":21.85535,"_sort":[1791435179403,21.85535,0,"90d4d761-77fc-4ee1-a191-43f0a9d716b9"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Maoyi Huang","hasEmail":"mailto:maoyi.huang@noaa.gov"},"description":"Serially complete forcing data for historical and future runs of the Community Land Model (CLM) for the Thurston and Olaa tower sites. The historical (205-2015) data are derived from measured time series at each site. All gaps were filled to create serially complete time series of each forcing variable. Gap filling was based on the best available information at each time step and made use of statistical relationships with available data, historical analogues and other methods. Future (2071-2100) forcing data sets were constructed by shifting values in the historical data set by increments selected for possible future scenarios. Increments were based on the results of statistical downscaling of future climate by Elison Timm et al. (2015, Statistical downscaling of rainfall changes in Hawai\u2018i based on the CMIP5 global model projections, Journal of Geophysical Research-Atmospheres 120: 92-112, doi: 10.1002/2014JD022059) and Elison Timm and Fortini (2016, Statistical estimation of future temperature anomalies, data product, http://www.atmos.albany.edu/facstaff/timm/products_data.html).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14THG69","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5bf49602e4b045bfcae26543.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5bf49602e4b045bfcae26543","keyword":["Hawai'i","Hawaii","Olaa","Thurston","USGS:5bf49602e4b045bfcae26543","climate change","community land model","ecosystem carbon exchange","environment","evapotranspiration","future scenarios","geospatial datasets"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-160.2361, 18.9155, -154.7986, 22.2344","theme":["geospatial"],"title":"Historical and future forcing data for the Community Land Model 4.0 used in two study sites in Hawai'i, 2005-2100"},"description":"Serially complete forcing data for historical and future runs of the Community Land Model (CLM) for the Thurston and Olaa tower sites. The historical (205-2015) data are derived from measured time series at each site. All gaps were filled to create serially complete time series of each forcing variable. Gap filling was based on the best available information at each time step and made use of statistical relationships with available data, historical analogues and other methods. Future (2071-2100) forcing data sets were constructed by shifting values in the historical data set by increments selected for possible future scenarios. Increments were based on the results of statistical downscaling of future climate by Elison Timm et al. (2015, Statistical downscaling of rainfall changes in Hawai\u2018i based on the CMIP5 global model projections, Journal of Geophysical Research-Atmospheres 120: 92-112, doi: 10.1002/2014JD022059) and Elison Timm and Fortini (2016, Statistical estimation of future temperature anomalies, data product, http://www.atmos.albany.edu/facstaff/timm/products_data.html).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/86e5fb8a-f429-4594-9a9f-38a2e503af13","harvest_record_raw":"https://catalog.data.gov/harvest_record/86e5fb8a-f429-4594-9a9f-38a2e503af13/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5bf49602e4b045bfcae26543","keyword":["Hawai'i","Hawaii","Olaa","Thurston","USGS:5bf49602e4b045bfcae26543","climate change","community land model","ecosystem carbon exchange","environment","evapotranspiration","future scenarios","geospatial datasets"],"last_harvested_date":"2026-10-08T04:52:59.403090","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"historical-and-future-forcing-data-for-the-community-land-model-4-0-used-in-two--2005-2100","spatial_centroid":{"lat":20.243060000000003,"lon":-158.06109999999998},"spatial_shape":{"coordinates":[[[-160.2361,18.9155],[-160.2361,22.2344],[-154.7986,22.2344],[-154.7986,18.9155],[-160.2361,18.9155]]],"type":"Polygon"},"theme":["geospatial"],"title":"Historical and future forcing data for the Community Land Model 4.0 used in two study sites in Hawai'i, 2005-2100","type":"dataset"},{"_score":46.496727,"_sort":[1791435061102,46.496727,0,"bef095f6-7d75-4ce1-b26b-4431344f8698"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jared Bowden","hasEmail":"mailto:jhbowden@ncsu.edu"},"description":"The Weather Research and Forecasting (WRF) model was used to downscale select general circulation models (GCMs) that participated in the Coupled Model Intercomparison Project (CMIP5). WRF was used to produce output at a 2-km horizontal resolution for Puerto Rico and the US Virgin Islands by applying a 30-km/10- km/2-km one-way nest. Data from each nest was archived. Two twenty-year time slices from two CMIP5 GCMs were dynamically downscaled using WRF, a historical time slice (1985-2005) and a future time slice (2040-2060). The CMIP5 GCMs downscaled included the Community Climate System Model (CCSM4) and the Centre National de Recherches Meteorologiques-CERFACS (CNRM). The high Greenhouse Gas Emission Scenario, RCP8.5, was downscaled for the future time slice. The output parameters saved were in response to the needs of physical scientists and other stakeholders within the islands, including ecologists, biologists, and hydrologists.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7GB23BW","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6776f387d34ee88ba0b1585d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f387d34ee88ba0b1585d","keyword":["Atmosphere","Meteorology","Puerto Rico","USGS:6776f387d34ee88ba0b1585d","Virgin Islands","climate change","climatology","climatologyMeteorologyAtmosphere","dataset","geospatial datasets","service","time"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-85.5395, 9.3143, -50.4605, 30.0082","theme":["geospatial"],"title":"Weather Research and Forecasting (WRF): Puerto Rico &amp; US Virgin Islands Dynamical Downscaled Climate Change Projections"},"description":"The Weather Research and Forecasting (WRF) model was used to downscale select general circulation models (GCMs) that participated in the Coupled Model Intercomparison Project (CMIP5). WRF was used to produce output at a 2-km horizontal resolution for Puerto Rico and the US Virgin Islands by applying a 30-km/10- km/2-km one-way nest. Data from each nest was archived. Two twenty-year time slices from two CMIP5 GCMs were dynamically downscaled using WRF, a historical time slice (1985-2005) and a future time slice (2040-2060). The CMIP5 GCMs downscaled included the Community Climate System Model (CCSM4) and the Centre National de Recherches Meteorologiques-CERFACS (CNRM). The high Greenhouse Gas Emission Scenario, RCP8.5, was downscaled for the future time slice. The output parameters saved were in response to the needs of physical scientists and other stakeholders within the islands, including ecologists, biologists, and hydrologists.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/19b5c58b-20ed-493f-945b-b1bf2a68a091","harvest_record_raw":"https://catalog.data.gov/harvest_record/19b5c58b-20ed-493f-945b-b1bf2a68a091/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f387d34ee88ba0b1585d","keyword":["Atmosphere","Meteorology","Puerto Rico","USGS:6776f387d34ee88ba0b1585d","Virgin Islands","climate change","climatology","climatologyMeteorologyAtmosphere","dataset","geospatial datasets","service","time"],"last_harvested_date":"2026-10-08T04:51:01.102660","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"weather-research-and-forecasting-wrf-puerto-rico-amp-us-virgin-islands-dynamical-downscale","spatial_centroid":{"lat":17.59186,"lon":-71.5079},"spatial_shape":{"coordinates":[[[-85.5395,9.3143],[-85.5395,30.0082],[-50.4605,30.0082],[-50.4605,9.3143],[-85.5395,9.3143]]],"type":"Polygon"},"theme":["geospatial"],"title":"Weather Research and Forecasting (WRF): Puerto Rico &amp; US Virgin Islands Dynamical Downscaled Climate Change Projections","type":"dataset"},{"_score":26.7462,"_sort":[1791434624294,26.7462,0,"205e4fa4-44fc-4ee6-98ad-cf9ac24ebe3d"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jose Pablo Ortiz Partida","hasEmail":"mailto:joportiz@ucdavis.edu"},"description":"Create an inventory of water-related models that have been developed for the Rio Grande/Bravo basin. The summary includes a description of model river extent, spatial and temporal resolution, time period, model type, and their possible application for testing environmental flows or climate change future alternatives.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/C9BC7D","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.58e52342e4b09da679997bf6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58e52342e4b09da679997bf6","keyword":["Rio Bravo","Rio Grande","USGS:58e52342e4b09da679997bf6","climate change","environment","geospatial datasets","hydraulic engineering","hydraulics","hydrologic","hydrologic processes","inlandWaters","optimization","river systems","simulation","water management"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.2410, 24.8092, -97.1392, 38.3621","theme":["geospatial"],"title":"River extent of water related models in the Rio Grande/Bravo basin"},"description":"Create an inventory of water-related models that have been developed for the Rio Grande/Bravo basin. The summary includes a description of model river extent, spatial and temporal resolution, time period, model type, and their possible application for testing environmental flows or climate change future alternatives.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/059c93bd-f3ff-4b07-84d4-589fa8a828fa","harvest_record_raw":"https://catalog.data.gov/harvest_record/059c93bd-f3ff-4b07-84d4-589fa8a828fa/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58e52342e4b09da679997bf6","keyword":["Rio Bravo","Rio Grande","USGS:58e52342e4b09da679997bf6","climate change","environment","geospatial datasets","hydraulic engineering","hydraulics","hydrologic","hydrologic processes","inlandWaters","optimization","river systems","simulation","water management"],"last_harvested_date":"2026-10-08T04:43:44.294312","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"river-extent-of-water-related-models-in-the-rio-grande-bravo-basin","spatial_centroid":{"lat":30.23036,"lon":-104.40028},"spatial_shape":{"coordinates":[[[-109.241,24.8092],[-109.241,38.3621],[-97.1392,38.3621],[-97.1392,24.8092],[-109.241,24.8092]]],"type":"Polygon"},"theme":["geospatial"],"title":"River extent of water related models in the Rio Grande/Bravo basin","type":"dataset"},{"_score":22.270481,"_sort":[1791434147414,22.270481,0,"51c8ca10-5d74-4f2a-bd4a-26e0aa7f9474"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jose Pablo Ortiz Partida","hasEmail":"mailto:joportiz@ucdavis.edu"},"description":"This project inventories and reviews available water resource models used to meet multiple (and often competing) water resource management objectives. Water resource modeling tools have been developed for many different regions and sub-basins of the Rio Grande/Bravo (RGB). Each of these tools have specific objectives, whether it is to explore drought mitigation alternatives, conflict resolution, climate change evaluation, tradeoff and economic synergies, water allocation, reservoir operations, or collaborative planning. We specifically evaluate the applicability of those models to evaluating trade-offs in meeting societal and environmental flow requirements to recover native ecosystems. This work communicates the state of the RGB science to diverse stakeholders, researchers, and decision-makers. It also identify information gaps that merit additional research and resources, describe promising future steps to couple and improve existing systems models, and propose ideas to share and serve science syntheses in a digital and spatially-explicit databases.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/C9BC7D","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.58e52181e4b09da679997bed.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58e52181e4b09da679997bed","keyword":["Rio Bravo","Rio Grande","USGS:58e52181e4b09da679997bed","climate change","environment","geospatial datasets","hydraulic engineering","hydraulics","hydrologic","hydrologic processes","inlandWaters","optimization","river systems","simulation","water management"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.2410, 24.8092, -97.1392, 38.3621","theme":["geospatial"],"title":"Assessing the State of Water Resource Knowledge and Tools for Future Planning in the Rio Grande-Rio Bravo Basin"},"description":"This project inventories and reviews available water resource models used to meet multiple (and often competing) water resource management objectives. Water resource modeling tools have been developed for many different regions and sub-basins of the Rio Grande/Bravo (RGB). Each of these tools have specific objectives, whether it is to explore drought mitigation alternatives, conflict resolution, climate change evaluation, tradeoff and economic synergies, water allocation, reservoir operations, or collaborative planning. We specifically evaluate the applicability of those models to evaluating trade-offs in meeting societal and environmental flow requirements to recover native ecosystems. This work communicates the state of the RGB science to diverse stakeholders, researchers, and decision-makers. It also identify information gaps that merit additional research and resources, describe promising future steps to couple and improve existing systems models, and propose ideas to share and serve science syntheses in a digital and spatially-explicit databases.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9b747f68-240f-4188-a9de-fe6b43ca542e","harvest_record_raw":"https://catalog.data.gov/harvest_record/9b747f68-240f-4188-a9de-fe6b43ca542e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58e52181e4b09da679997bed","keyword":["Rio Bravo","Rio Grande","USGS:58e52181e4b09da679997bed","climate change","environment","geospatial datasets","hydraulic engineering","hydraulics","hydrologic","hydrologic processes","inlandWaters","optimization","river systems","simulation","water management"],"last_harvested_date":"2026-10-08T04:35:47.414724","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"assessing-the-state-of-water-resource-knowledge-and-tools-for-future-planning-in-the-rio-g","spatial_centroid":{"lat":30.23036,"lon":-104.40028},"spatial_shape":{"coordinates":[[[-109.241,24.8092],[-109.241,38.3621],[-97.1392,38.3621],[-97.1392,24.8092],[-109.241,24.8092]]],"type":"Polygon"},"theme":["geospatial"],"title":"Assessing the State of Water Resource Knowledge and Tools for Future Planning in the Rio Grande-Rio Bravo Basin","type":"dataset"},{"_score":26.938204,"_sort":[1791433278925,26.938204,0,"1b098829-f83c-44b0-bb90-9e4d2742159c"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jessica L. Ball","hasEmail":"mailto:jlball@usgs.gov"},"description":"This dataset contains dates, geographic locations, and meteorological data associated with non-eruptive debris flows on Mount Shasta. A non-eruptive debris flow an alternate term for volcanic mudflow or lahar. This dataset was derived from existing scientific literature, historical California newspaper records, and open-access weather and climate data.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14JLMT6","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a08bf2bb66b01afd44d9006.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a08bf2bb66b01afd44d9006","keyword":["California","Mount Shasta","USGS:6a08bf2bb66b01afd44d9006","Volcano","biota","climate","debris flow","debris flow trigger","lahar","landslides","precipitation","snow and ice melt","volcano"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.6294, 41.0711, -121.7450, 41.7795","theme":["geospatial"],"title":"Dates, locations, and local meteorological conditions associated with non-eruptive debris flows on Mount Shasta from 1900-2025"},"description":"This dataset contains dates, geographic locations, and meteorological data associated with non-eruptive debris flows on Mount Shasta. A non-eruptive debris flow an alternate term for volcanic mudflow or lahar. This dataset was derived from existing scientific literature, historical California newspaper records, and open-access weather and climate data.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a91096ec-2df2-41d5-a17c-d0a8417afc24","harvest_record_raw":"https://catalog.data.gov/harvest_record/a91096ec-2df2-41d5-a17c-d0a8417afc24/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a08bf2bb66b01afd44d9006","keyword":["California","Mount Shasta","USGS:6a08bf2bb66b01afd44d9006","Volcano","biota","climate","debris flow","debris flow trigger","lahar","landslides","precipitation","snow and ice melt","volcano"],"last_harvested_date":"2026-10-08T04:21:18.925384","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"dates-locations-and-local-meteorological-conditions-associated-with-non-eruptive-1900-2025","spatial_centroid":{"lat":41.35446,"lon":-122.27564},"spatial_shape":{"coordinates":[[[-122.6294,41.0711],[-122.6294,41.7795],[-121.745,41.7795],[-121.745,41.0711],[-122.6294,41.0711]]],"type":"Polygon"},"theme":["geospatial"],"title":"Dates, locations, and local meteorological conditions associated with non-eruptive debris flows on Mount Shasta from 1900-2025","type":"dataset"},{"_score":22.033459,"_sort":[1791433009557,22.033459,0,"9f0f3762-9b4b-49e1-b5b2-880d27af9179"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Ryan Srivier","hasEmail":"mailto:rsriver@illinois.edu"},"description":"These files contain spatial fields of simulated yearly thermosteric sea-level anomalies between 1951-2100 (referenced to year 1800) from a perturbed physics ensemble experiment using the University of Victoria Earth System Model (UVic). Thermosteric sea-level anomalies are derived from changes in density, which are calculated using the model's temperature and salinity fields. The ensemble varies 3 parameters controlling the model's equilibrium climate sensitivity (CS), background vertical diffusivity in the ocean (KV), and radiative effects of atmospheric sulfate aerosols (ASC). These parameters are varied over the following ranges: CS-(1.1, 1.6, 2.2, 2.6, 3.1, 4.0, 5.4, 6.5, 8.2, 11.2); KV-(0.1, 0.2, 0.3, 0.4, 0.5); ASC-(0.00, 0.75, 1.50, 2.25, 3.00). The unique parameter combination for each ensemble member is reflected in the variable name, where we have multiplied the parameter values by 100 to display as integer values. For the projection phase of the ensemble (2005-2100), we use atmospheric forcings from the RCP8.5 scenario. These forcings include atmospheric CO2 concentrations, radiative forcings from non-CO2 greenhouse gases, and spatially resolved changes in albedo associated with direct effects of sulfate aerosols. See Sriver et al., 2012 for complete discussion.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/slr2d","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6776fc30d34ee88ba0b15956.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776fc30d34ee88ba0b15956","keyword":["Thermosteric","USGS:6776fc30d34ee88ba0b15956","climate change","climatologyMeteorologyAtmosphere","geospatial datasets","sea level rise"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -90.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"Thermosteric Sea Level Rise Projections with Parametric Uncertainty"},"description":"These files contain spatial fields of simulated yearly thermosteric sea-level anomalies between 1951-2100 (referenced to year 1800) from a perturbed physics ensemble experiment using the University of Victoria Earth System Model (UVic). Thermosteric sea-level anomalies are derived from changes in density, which are calculated using the model's temperature and salinity fields. The ensemble varies 3 parameters controlling the model's equilibrium climate sensitivity (CS), background vertical diffusivity in the ocean (KV), and radiative effects of atmospheric sulfate aerosols (ASC). These parameters are varied over the following ranges: CS-(1.1, 1.6, 2.2, 2.6, 3.1, 4.0, 5.4, 6.5, 8.2, 11.2); KV-(0.1, 0.2, 0.3, 0.4, 0.5); ASC-(0.00, 0.75, 1.50, 2.25, 3.00). The unique parameter combination for each ensemble member is reflected in the variable name, where we have multiplied the parameter values by 100 to display as integer values. For the projection phase of the ensemble (2005-2100), we use atmospheric forcings from the RCP8.5 scenario. These forcings include atmospheric CO2 concentrations, radiative forcings from non-CO2 greenhouse gases, and spatially resolved changes in albedo associated with direct effects of sulfate aerosols. See Sriver et al., 2012 for complete discussion.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b19cf796-645a-49ad-8db5-878abfc22e01","harvest_record_raw":"https://catalog.data.gov/harvest_record/b19cf796-645a-49ad-8db5-878abfc22e01/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776fc30d34ee88ba0b15956","keyword":["Thermosteric","USGS:6776fc30d34ee88ba0b15956","climate change","climatologyMeteorologyAtmosphere","geospatial datasets","sea level rise"],"last_harvested_date":"2026-10-08T04:16:49.557817","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"thermosteric-sea-level-rise-projections-with-parametric-uncertainty","spatial_centroid":{"lat":-18.0,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-90.0],[-180.0,90.0],[180.0,90.0],[180.0,-90.0],[-180.0,-90.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Thermosteric Sea Level Rise Projections with Parametric Uncertainty","type":"dataset"},{"_score":8.913576,"_sort":[1791432636356,8.913576,0,"81228f98-175c-4263-acaf-476aec22b7ab"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jose Pablo Ortiz Partida","hasEmail":"mailto:joportiz@ucdavis.edu"},"description":"The dataset is a selection of water related models in the Rio Grande/Bravo basin that could be considered on the development or testing of environmental flow targets in the basin.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/C9BC7D","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.58e53848e4b09da679997cef.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58e53848e4b09da679997cef","keyword":["Rio Bravo","Rio Grande","USGS:58e53848e4b09da679997cef","climate change","environment","geospatial datasets","hydraulic engineering","hydraulics","hydrologic","hydrologic processes","inlandWaters","optimization","river systems","simulation","water management"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.2410, 24.8092, -97.1392, 38.3621","theme":["geospatial"],"title":"River extent of water related models in the Rio Grande/Bravo basin to test environmental flows"},"description":"The dataset is a selection of water related models in the Rio Grande/Bravo basin that could be considered on the development or testing of environmental flow targets in the basin.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/86ed63ea-4241-450e-b920-fc5b8f2caa2c","harvest_record_raw":"https://catalog.data.gov/harvest_record/86ed63ea-4241-450e-b920-fc5b8f2caa2c/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58e53848e4b09da679997cef","keyword":["Rio Bravo","Rio Grande","USGS:58e53848e4b09da679997cef","climate change","environment","geospatial datasets","hydraulic engineering","hydraulics","hydrologic","hydrologic processes","inlandWaters","optimization","river systems","simulation","water management"],"last_harvested_date":"2026-10-08T04:10:36.356874","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"river-extent-of-water-related-models-in-the-rio-grande-bravo-basin-to-test-environmental-f","spatial_centroid":{"lat":30.23036,"lon":-104.40028},"spatial_shape":{"coordinates":[[[-109.241,24.8092],[-109.241,38.3621],[-97.1392,38.3621],[-97.1392,24.8092],[-109.241,24.8092]]],"type":"Polygon"},"theme":["geospatial"],"title":"River extent of water related models in the Rio Grande/Bravo basin to test environmental flows","type":"dataset"},{"_score":51.13057,"_sort":[1791431133757,51.13057,0,"e68fcd60-bdb2-4770-acc9-f93ab39e9e8e"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Xiaoming Hu","hasEmail":"mailto:xhu@ou.edu"},"description":"The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results (with a 4km horizontal resolution) to investigate the precipitation variability near the Edwards Plateau and Balcones Escarpment in Texas, an area prone to heavy rain and devastating flood events. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. As a result, a better precipitation downscaling is achieved. With the carefully designed configurations, WRF downscaling is conducted for 1980-2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans-state Oologah watershed is also achieved. In addition, analyzing the high-resolution (4 km) downscaling outputs leads to a better understanding regarding the precipitation variability in Texas.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/red_river_2018","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6776f334d34ee88ba0b15855.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f334d34ee88ba0b15855","keyword":["USGS:6776f334d34ee88ba0b15855","climate change","climatologyMeteorologyAtmosphere","downscaling","geospatial datasets"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-133.0100, 20.9900, -65.2200, 52.8400","theme":["geospatial"],"title":"Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System"},"description":"The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results (with a 4km horizontal resolution) to investigate the precipitation variability near the Edwards Plateau and Balcones Escarpment in Texas, an area prone to heavy rain and devastating flood events. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. As a result, a better precipitation downscaling is achieved. With the carefully designed configurations, WRF downscaling is conducted for 1980-2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans-state Oologah watershed is also achieved. In addition, analyzing the high-resolution (4 km) downscaling outputs leads to a better understanding regarding the precipitation variability in Texas.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/13e1878c-66ad-406c-8aa4-18097c067737","harvest_record_raw":"https://catalog.data.gov/harvest_record/13e1878c-66ad-406c-8aa4-18097c067737/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f334d34ee88ba0b15855","keyword":["USGS:6776f334d34ee88ba0b15855","climate change","climatologyMeteorologyAtmosphere","downscaling","geospatial datasets"],"last_harvested_date":"2026-10-08T03:45:33.757341","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"very-high-resolution-dynamic-downscaling-of-regional-climate-for-use-in-long-term-hydrolog","spatial_centroid":{"lat":33.730000000000004,"lon":-105.894},"spatial_shape":{"coordinates":[[[-133.01,20.99],[-133.01,52.84],[-65.22,52.84],[-65.22,20.99],[-133.01,20.99]]],"type":"Polygon"},"theme":["geospatial"],"title":"Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System","type":"dataset"},{"_score":28.863003,"_sort":[1791430809699,28.863003,11,"b203a1d7-4efa-4deb-92ec-d1f35e3c0ad0"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Carlos Gaitan","hasEmail":"mailto:carlos.gaitan@ou.edu"},"description":"This collection contains three statistically downscaled time series (datasets) for the Red River Basin (South Central U.S.), and one dataset used as historical observations. In particular, three different Global Climate Models (MPI-ESM-LR, CCSM4 and MIROC5) were downscaled using three different quantile mapping methods (CDFt, EDQM and BCQM). We do not recommend the use of the BCQM method, as the CDFt method is considered an improvement of it. The datasets created using the BCQM method are published as a demonstration of the risks of using flawed methods. The variables of interest are: daily maximum and minimum temperature, and daily precipitation. The spatial resolution of the datasets in the collection is 1/10th of a degree (~ 11 km). The statistically downscaled datasets include local climate projections of three different Representative Concentration Pathways (RCPs - 2.6, 4.5 and 8.5) for the 21st century (2006 \u2013 2099), and for the historical (1961-2005) period.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/RedRiver","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6776f0a3d34ee88ba0b15825.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f0a3d34ee88ba0b15825","keyword":["CMIP5","Meteorology","Red River","South Central U.S.","USGS:6776f0a3d34ee88ba0b15825","air temperature","atmosphere","climate change","climatology","climatologyMeteorologyAtmosphere","downscaled climate projection","geospatial datasets","historical","latitude","longitude","maximum temperature","minimum temperature","precipitation","precipitation flux","rcp45","rcp85","time"],"modified":"2026-10-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-108.9500, 26.0500, -90.0500, 39.9500","theme":["geospatial"],"title":"Statistically downscaled estimates of precipitation and temperature for the Red River basin (South Central U.S.A) Downscaled Future Projections"},"description":"This collection contains three statistically downscaled time series (datasets) for the Red River Basin (South Central U.S.), and one dataset used as historical observations. In particular, three different Global Climate Models (MPI-ESM-LR, CCSM4 and MIROC5) were downscaled using three different quantile mapping methods (CDFt, EDQM and BCQM). We do not recommend the use of the BCQM method, as the CDFt method is considered an improvement of it. The datasets created using the BCQM method are published as a demonstration of the risks of using flawed methods. The variables of interest are: daily maximum and minimum temperature, and daily precipitation. The spatial resolution of the datasets in the collection is 1/10th of a degree (~ 11 km). The statistically downscaled datasets include local climate projections of three different Representative Concentration Pathways (RCPs - 2.6, 4.5 and 8.5) for the 21st century (2006 \u2013 2099), and for the historical (1961-2005) period.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/55d3e0f7-c841-4675-893f-095fd93fce54","harvest_record_raw":"https://catalog.data.gov/harvest_record/55d3e0f7-c841-4675-893f-095fd93fce54/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f0a3d34ee88ba0b15825","keyword":["CMIP5","Meteorology","Red River","South Central U.S.","USGS:6776f0a3d34ee88ba0b15825","air temperature","atmosphere","climate change","climatology","climatologyMeteorologyAtmosphere","downscaled climate projection","geospatial datasets","historical","latitude","longitude","maximum temperature","minimum temperature","precipitation","precipitation flux","rcp45","rcp85","time"],"last_harvested_date":"2026-10-08T03:40:09.699053","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":11,"publisher":"U.S. Geological Survey","slug":"statistically-downscaled-estimates-of-precipitation-and-temperature-for-the-red-river-basi","spatial_centroid":{"lat":31.610000000000003,"lon":-101.39},"spatial_shape":{"coordinates":[[[-108.95,26.05],[-108.95,39.95],[-90.05,39.95],[-90.05,26.05],[-108.95,26.05]]],"type":"Polygon"},"theme":["geospatial"],"title":"Statistically downscaled estimates of precipitation and temperature for the Red River basin (South Central U.S.A) Downscaled Future Projections","type":"dataset"},{"_score":9.273021,"_sort":[1791412715074,9.273021,0,"e356e550-c63e-4ff9-a61a-8b00a6ee2c33"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430912?Reference=2172682","format":"pdf","mediaType":"application/pdf","title":"1955_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172682","issued":"1955-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172682","modified":"1955-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1955-01-01/1955-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1955 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1955_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/82532ccf-e58e-452c-bad9-82dd5c1f12e3","harvest_record_raw":"https://catalog.data.gov/harvest_record/82532ccf-e58e-452c-bad9-82dd5c1f12e3/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172682","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:35.074728","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1955-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1955 Bar Harbor, Maine","type":"dataset"},{"_score":9.284188,"_sort":[1791412714712,9.284188,0,"cfeaa1eb-50a8-4ba4-9c34-3313f07a218e"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/444274?Reference=2172683","format":"pdf","mediaType":"application/pdf","title":"1954_River_and_Climatological_Obsevations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172683","issued":"1954-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172683","modified":"1954-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1954-01-01/1954-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1954 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1954_River_and_Climatological_Obsevations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/ddecaa77-cecc-4d84-a75c-8c2841deb2c7","harvest_record_raw":"https://catalog.data.gov/harvest_record/ddecaa77-cecc-4d84-a75c-8c2841deb2c7/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172683","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:34.712031","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1954-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1954 Bar Harbor, Maine","type":"dataset"},{"_score":9.246872,"_sort":[1791412714519,9.246872,0,"d187fbda-5e45-4c44-b19a-ec99f17daf6c"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430916?Reference=2172680","format":"pdf","mediaType":"application/pdf","title":"1957_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172680","issued":"1957-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172680","modified":"1957-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1957-01-01/1957-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1957 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1957_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/4413bb17-922e-4fe3-9bef-6f6ca6667ef3","harvest_record_raw":"https://catalog.data.gov/harvest_record/4413bb17-922e-4fe3-9bef-6f6ca6667ef3/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172680","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:34.519302","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1957-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1957 Bar Harbor, Maine","type":"dataset"},{"_score":9.273021,"_sort":[1791412714260,9.273021,0,"b140f2de-52bf-4c28-8ade-d22e22f1caae"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430914?Reference=2172681","format":"pdf","mediaType":"application/pdf","title":"1956_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172681","issued":"1956-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172681","modified":"1956-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1956-01-01/1956-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1956 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1956_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/da9550fa-728b-4de7-9b31-98a2232f48d8","harvest_record_raw":"https://catalog.data.gov/harvest_record/da9550fa-728b-4de7-9b31-98a2232f48d8/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172681","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:34.260504","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1956-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1956 Bar Harbor, Maine","type":"dataset"},{"_score":9.246872,"_sort":[1791412693821,9.246872,0,"6065ef08-a7db-4e07-99de-59845d2621c3"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430952?Reference=2172671","format":"pdf","mediaType":"application/pdf","title":"2172671,_1964_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172671","issued":"1964-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172671","modified":"1964-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1964-01-01/1964-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1964 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["2172671,_1964_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/892ba1da-3d88-4a91-b1a7-4fcfdc781d15","harvest_record_raw":"https://catalog.data.gov/harvest_record/892ba1da-3d88-4a91-b1a7-4fcfdc781d15/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172671","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:13.821374","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1964-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1964 Bar Harbor, Maine","type":"dataset"},{"_score":9.273021,"_sort":[1791412693636,9.273021,0,"69495bba-122a-4074-92dc-922d5be5e30c"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430946?Reference=2172672","format":"pdf","mediaType":"application/pdf","title":"2172672,_1963_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172672","issued":"1963-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172672","modified":"1963-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1963-01-01/1963-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1963 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["2172672,_1963_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/94347116-e5f9-495e-a3df-b5a3d3474e30","harvest_record_raw":"https://catalog.data.gov/harvest_record/94347116-e5f9-495e-a3df-b5a3d3474e30/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172672","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:13.636345","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1963-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1963 Bar Harbor, Maine","type":"dataset"},{"_score":9.273021,"_sort":[1791412693386,9.273021,0,"23795502-3dd7-438a-b80f-a3437c429264"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430962?Reference=2172670","format":"pdf","mediaType":"application/pdf","title":"2172670,_1965_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172670","issued":"1965-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172670","modified":"1965-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1965-01-01/1965-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1965 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["2172670,_1965_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/562043af-c01e-47ee-b0fb-2e53256a6035","harvest_record_raw":"https://catalog.data.gov/harvest_record/562043af-c01e-47ee-b0fb-2e53256a6035/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172670","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:13.386084","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1965-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1965 Bar Harbor, Maine","type":"dataset"},{"_score":9.246872,"_sort":[1791412693022,9.246872,0,"0a982589-6bcf-48d9-a1a1-5855e3c3c5ad"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430927?Reference=2172676","format":"pdf","mediaType":"application/pdf","title":"1960_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172676","issued":"1960-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172676","modified":"1960-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1960-01-01/1960-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1960 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1960_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/6a6c899f-9a2f-482b-953c-3c4c6595f799","harvest_record_raw":"https://catalog.data.gov/harvest_record/6a6c899f-9a2f-482b-953c-3c4c6595f799/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172676","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:13.022997","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1960-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1960 Bar Harbor, Maine","type":"dataset"},{"_score":9.309671,"_sort":[1791412692746,9.309671,0,"ad081ad0-17f5-4ab3-9544-1a2d76945dfe"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430944?Reference=2172673","format":"pdf","mediaType":"application/pdf","title":"1962_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172673","issued":"1962-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172673","modified":"1962-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1962-01-01/1962-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1962 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1962_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/ce9b16b7-d40c-4827-ad66-31c3cf6cba7f","harvest_record_raw":"https://catalog.data.gov/harvest_record/ce9b16b7-d40c-4827-ad66-31c3cf6cba7f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172673","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:12.746750","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1962-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1962 Bar Harbor, Maine","type":"dataset"},{"_score":9.246872,"_sort":[1791412692529,9.246872,0,"1fef810f-efa4-488d-838a-0c369a51e119"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430936?Reference=2172674","format":"pdf","mediaType":"application/pdf","title":"1961_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172674","issued":"1961-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172674","modified":"1961-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1961-01-01/1961-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1961 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1961_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/fb08306d-3a59-40bb-828f-0d1fc8b77995","harvest_record_raw":"https://catalog.data.gov/harvest_record/fb08306d-3a59-40bb-828f-0d1fc8b77995/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172674","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:12.529252","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1961-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1961 Bar Harbor, Maine","type":"dataset"},{"_score":9.23712,"_sort":[1791412692271,9.23712,0,"c23bf19c-b9df-42db-8d63-fdbf6e569780"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430918?Reference=2172679","format":"pdf","mediaType":"application/pdf","title":"1958_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172679","issued":"1958-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172679","modified":"1958-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1958-01-01/1958-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1958 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1958_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/d058b489-8898-497c-92aa-443c2934f404","harvest_record_raw":"https://catalog.data.gov/harvest_record/d058b489-8898-497c-92aa-443c2934f404/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172679","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:12.271126","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1958-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1958 Bar Harbor, Maine","type":"dataset"},{"_score":9.284188,"_sort":[1791412691786,9.284188,0,"55c5a20a-d3cc-49ec-b833-4ee5d9e4c79d"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430923?Reference=2172678","format":"pdf","mediaType":"application/pdf","title":"1959_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172678","issued":"1959-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172678","modified":"1959-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1959-01-01/1959-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1959 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["1959_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/17c864a2-0fca-4d51-b133-7a84050dfe94","harvest_record_raw":"https://catalog.data.gov/harvest_record/17c864a2-0fca-4d51-b133-7a84050dfe94/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172678","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:11.786982","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1959-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1959 Bar Harbor, Maine","type":"dataset"},{"_score":9.284188,"_sort":[1791412690774,9.284188,0,"5a016aff-df5a-4779-a2eb-6135632446a6"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430679?Reference=2172668","format":"pdf","mediaType":"application/pdf","title":"2172668,_River_and_Climatological_Observations,_1967.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172668","issued":"1967-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172668","modified":"1967-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1967-01-01/1967-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1967 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["2172668,_River_and_Climatological_Observations,_1967.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/1afb75a6-2b1c-44a0-8b07-7b4ad927f32e","harvest_record_raw":"https://catalog.data.gov/harvest_record/1afb75a6-2b1c-44a0-8b07-7b4ad927f32e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172668","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:10.774124","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1967-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1967 Bar Harbor, Maine","type":"dataset"},{"_score":9.309671,"_sort":[1791412690588,9.309671,0,"d9e482c5-9de5-4696-a7d0-548eb4d40d3e"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430965?Reference=2172669","format":"pdf","mediaType":"application/pdf","title":"2172669,_1966_River_and_Climatological_Observations.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172669","issued":"1966-01-01T00:00:00Z","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172669","modified":"1966-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1966-01-01/1966-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1966 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["2172669,_1966_River_and_Climatological_Observations.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/488968d5-5f28-4efc-9438-8a98e0c988e0","harvest_record_raw":"https://catalog.data.gov/harvest_record/488968d5-5f28-4efc-9438-8a98e0c988e0/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172669","keyword":["Air Temperature","Bar Harbor","Climate","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:10.588950","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1966-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1966 Bar Harbor, Maine","type":"dataset"},{"_score":9.23712,"_sort":[1791412690401,9.23712,0,"17c9b55a-5f5f-4ca1-8b6c-68e156d464f1"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:24"],"contactPoint":{"@type":"vcard:Contact","fn":"NPS IRMA Help","hasEmail":"mailto:NRSS_DataStore@nps.gov"},"description":"Daily climatological records for Bar Harbor, Maine","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://irma.nps.gov/DataStore/DownloadFile/430672?Reference=2172667","format":"pdf","mediaType":"application/pdf","title":"2172667,_River_and_Climatological_Observations,_1968.pdf"}],"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172667","issued":"1968-01-01T00:00:00Z","keyword":["?Climate","Air Temperature","Bar Harbor","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"landingPage":"https://irma.nps.gov/DataStore/Reference/Profile/2172667","modified":"1968-01-01T00:00:00Z","programCode":["010:118","010:119"],"publisher":{"@type":"org:Organization","name":"National Park Service"},"spatial":"-68.65984,44.00588,-68.03728,44.4314156","temporal":"1968-01-01/1968-12-01","theme":["Tabular Dataset"],"title":"Record of climatological observations - 1968 Bar Harbor, Maine"},"description":"Daily climatological records for Bar Harbor, Maine","distribution_titles":["2172667,_River_and_Climatological_Observations,_1968.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/21860a86-7386-4d0a-bffb-9d0c31c224ab","harvest_record_raw":"https://catalog.data.gov/harvest_record/21860a86-7386-4d0a-bffb-9d0c31c224ab/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/NPS_DataStore_2172667","keyword":["?Climate","Air Temperature","Bar Harbor","National Oceananic and Atmospheric Administration","National Weather Service","Precipitation","U.S. Department of Commerce","Weather","Weather Bureau","fog"],"last_harvested_date":"2026-10-07T22:38:10.401558","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"National Park Service","slug":"record-of-climatological-observations-1968-bar-harbor-maine","spatial_centroid":{"lat":44.17609424,"lon":-68.410816},"spatial_shape":{"coordinates":[[[-68.65984,44.00588],[-68.65984,44.4314156],[-68.03728,44.4314156],[-68.03728,44.00588],[-68.65984,44.00588]]],"type":"Polygon"},"theme":["Tabular Dataset"],"title":"Record of climatological observations - 1968 Bar Harbor, Maine","type":"dataset"},{"_score":35.29424,"_sort":[1791408974596,35.29424,3,"d796eb29-f37d-4d9d-88e6-bf2072b34dd1"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:04"],"contactPoint":{"@type":"vcard:Contact","fn":"Bureau of Land Management, BLM_OC_REA_Data_Portal_Feedback_Team","hasEmail":"mailto:BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov"},"description":"<div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>The Rapid Ecoregional Assessments (REAs) were launched in 2010 to help improve the understanding of existing condition for ecoregions and how conditions may be altered by ongoing environmental changes and land use. They do not allocate resource uses or make management decisions. They provide science-based information and tools for land managers and stakeholders to consider in subsequent resource planning and decision making processes. REAs examine ecological values, conditions, and trends within large, connected areas that have similar environmental characteristics in order to capitalize on efficiency of scale. REAs provide context to make site-specific decisions and conduct analyses for resource management plans and National Environmental Policy Act processes. The REAs seek to identify important resource values and patterns of environmental change that may not be evident when managing smaller, local land areas.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>This report contains the final climate change supplemental report for the Northern Great Basin Rapid Ecoregional Assessment.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>Contact: BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov</div>","distribution":[{"@type":"dcat:Distribution","accessURL":"https://blm-egis.maps.arcgis.com/sharing/rest/content/items/f75f018d7af044f3b0431108c66f87a8/data","format":"PDF","mediaType":"application/pdf","title":"PDF Document"},{"@type":"dcat:Distribution","accessURL":"https://blm-egis.maps.arcgis.com/sharing/rest/content/items/f75f018d7af044f3b0431108c66f87a8/info/metadata/metadata.xml?format=iso19139","mediaType":"text/xml","title":"ISO-19139 metadata"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/documents/BLM-EGIS::blm-natl-rea-northern-great-basin-rapid-ecoregional-assessment-rea-climate-change-supplemental-report-november-2016","format":"Web Page","mediaType":"text/html","title":"ArcGIS Hub Dataset"}],"identifier":"https://blm-egis.maps.arcgis.com/home/item.html?id=f75f018d7af044f3b0431108c66f87a8","issued":"2022-10-28T21:30:37Z","keyword":["2016","BLM","Boundaries","Bureau of Land Management","California","DOI","Management","NGB 2016","Nevada","Northern Great Basin","Oregon","REA","Rapid Ecoregional Assessment","Report","Utah","Wyoming","environment","planning"],"landingPage":"https://gbp-blm-egis.hub.arcgis.com/documents/BLM-EGIS::blm-natl-rea-northern-great-basin-rapid-ecoregional-assessment-rea-climate-change-supplemental-report-november-2016","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2024-06-22T08:31:15Z","programCode":["010:000"],"publisher":{"@type":"org:Organization","name":"Bureau of Land Management"},"spatial":"-124.4783,29.0842,-82.6747,49.0004","theme":["non-geospatial"],"title":"BLM Natl REA Northern Great Basin Rapid Ecoregional Assessment (REA) Climate Change Supplemental Report - November 2016"},"description":"<div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>The Rapid Ecoregional Assessments (REAs) were launched in 2010 to help improve the understanding of existing condition for ecoregions and how conditions may be altered by ongoing environmental changes and land use. They do not allocate resource uses or make management decisions. They provide science-based information and tools for land managers and stakeholders to consider in subsequent resource planning and decision making processes. REAs examine ecological values, conditions, and trends within large, connected areas that have similar environmental characteristics in order to capitalize on efficiency of scale. REAs provide context to make site-specific decisions and conduct analyses for resource management plans and National Environmental Policy Act processes. The REAs seek to identify important resource values and patterns of environmental change that may not be evident when managing smaller, local land areas.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>This report contains the final climate change supplemental report for the Northern Great Basin Rapid Ecoregional Assessment.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>Contact: BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov</div>","distribution_titles":["PDF Document","ISO-19139 metadata","ArcGIS Hub Dataset"],"harvest_record":"https://catalog.data.gov/harvest_record/018ff158-b8a8-4ab5-a5b2-765b51d1eaea","harvest_record_raw":"https://catalog.data.gov/harvest_record/018ff158-b8a8-4ab5-a5b2-765b51d1eaea/raw","has_download":false,"has_spatial":true,"identifier":"https://blm-egis.maps.arcgis.com/home/item.html?id=f75f018d7af044f3b0431108c66f87a8","keyword":["2016","BLM","Boundaries","Bureau of Land Management","California","DOI","Management","NGB 2016","Nevada","Northern Great Basin","Oregon","REA","Rapid Ecoregional Assessment","Report","Utah","Wyoming","environment","planning"],"last_harvested_date":"2026-10-07T21:36:14.596803","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":3,"publisher":"Bureau of Land Management","slug":"blm-natl-rea-northern-great-basin-rapid-ecoregional-assessment-rea-climate-change-sup-2016","spatial_centroid":{"lat":37.05068,"lon":-107.75686},"spatial_shape":{"coordinates":[[[-124.4783,29.0842],[-124.4783,49.0004],[-82.6747,49.0004],[-82.6747,29.0842],[-124.4783,29.0842]]],"type":"Polygon"},"theme":["non-geospatial"],"title":"BLM Natl REA Northern Great Basin Rapid Ecoregional Assessment (REA) Climate Change Supplemental Report - November 2016","type":"dataset"},{"_score":29.075317,"_sort":[1791408964672,29.075317,3,"46cfaed2-82eb-426a-a34f-41af47dba1e9"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:04"],"contactPoint":{"@type":"vcard:Contact","fn":"Bureau of Land Management, BLM_OC_REA_Data_Portal_Feedback_Team","hasEmail":"mailto:BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov"},"description":"<div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>The Rapid Ecoregional Assessments (REAs) were launched in 2010 to help improve the understanding of existing condition for ecoregions and how conditions may be altered by ongoing environmental changes and land use. They do not allocate resource uses or make management decisions. They provide science-based information and tools for land managers and stakeholders to consider in subsequent resource planning and decision making processes. REAs examine ecological values, conditions, and trends within large, connected areas that have similar environmental characteristics in order to capitalize on efficiency of scale. REAs provide context to make site-specific decisions and conduct analyses for resource management plans and National Environmental Policy Act processes. The REAs seek to identify important resource values and patterns of environmental change that may not be evident when managing smaller, local land areas.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>This document includes Appendices F, G, and H for the Northwestern Plains Rapid Ecoregional Assessment providing information about a data quality evaluation, ecological intactness, and NatureServe Climate Change Vulnerability Index (NSCCVI) reports for the ecoregion.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>Contact: BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov</div>","distribution":[{"@type":"dcat:Distribution","accessURL":"https://blm-egis.maps.arcgis.com/sharing/rest/content/items/350139b5ed2d4e10ae5223cd64e92454/data","format":"PDF","mediaType":"application/pdf","title":"PDF Document"},{"@type":"dcat:Distribution","accessURL":"https://blm-egis.maps.arcgis.com/sharing/rest/content/items/350139b5ed2d4e10ae5223cd64e92454/info/metadata/metadata.xml?format=iso19139","mediaType":"text/xml","title":"ISO-19139 metadata"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/documents/BLM-EGIS::blm-natl-rea-northwestern-plains-rapid-ecoregional-assessment-rea-appendices-f-g-and-h-data-quality-evaluation-ecological-intactness-and-natureserve-climate-change-vulnerability-index-nsccvi-reports-december-2012","format":"Web Page","mediaType":"text/html","title":"ArcGIS Hub Dataset"}],"identifier":"https://blm-egis.maps.arcgis.com/home/item.html?id=350139b5ed2d4e10ae5223cd64e92454","issued":"2022-10-28T22:58:25Z","keyword":["2012","BLM","Boundaries","Bureau of Land Management","DOI","Management","Montana","NWP 2012","North Dakota","Northwestern Plains","REA","Rapid Ecoregional Assessment","Report","South Dakota","Wyoming","environment","planning"],"landingPage":"https://gbp-blm-egis.hub.arcgis.com/documents/BLM-EGIS::blm-natl-rea-northwestern-plains-rapid-ecoregional-assessment-rea-appendices-f-g-and-h-data-quality-evaluation-ecological-intactness-and-natureserve-climate-change-vulnerability-index-nsccvi-reports-december-2012","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2024-06-22T08:31:33Z","programCode":["010:000"],"publisher":{"@type":"org:Organization","name":"Bureau of Land Management"},"spatial":"-124.4783,29.0842,-82.6747,49.0004","theme":["non-geospatial"],"title":"BLM Natl REA Northwestern Plains Rapid Ecoregional Assessment (REA) Appendices F - G and H Data Quality Evaluation - Ecological Intactness and NatureServe Climate Change Vulnerability Index (NSCCVI) Reports - December 2012"},"description":"<div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>The Rapid Ecoregional Assessments (REAs) were launched in 2010 to help improve the understanding of existing condition for ecoregions and how conditions may be altered by ongoing environmental changes and land use. They do not allocate resource uses or make management decisions. They provide science-based information and tools for land managers and stakeholders to consider in subsequent resource planning and decision making processes. REAs examine ecological values, conditions, and trends within large, connected areas that have similar environmental characteristics in order to capitalize on efficiency of scale. REAs provide context to make site-specific decisions and conduct analyses for resource management plans and National Environmental Policy Act processes. The REAs seek to identify important resource values and patterns of environmental change that may not be evident when managing smaller, local land areas.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>This document includes Appendices F, G, and H for the Northwestern Plains Rapid Ecoregional Assessment providing information about a data quality evaluation, ecological intactness, and NatureServe Climate Change Vulnerability Index (NSCCVI) reports for the ecoregion.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>Contact: BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov</div>","distribution_titles":["PDF Document","ISO-19139 metadata","ArcGIS Hub Dataset"],"harvest_record":"https://catalog.data.gov/harvest_record/e85d54e4-3537-4b5f-be56-1e4a268b0a4f","harvest_record_raw":"https://catalog.data.gov/harvest_record/e85d54e4-3537-4b5f-be56-1e4a268b0a4f/raw","has_download":false,"has_spatial":true,"identifier":"https://blm-egis.maps.arcgis.com/home/item.html?id=350139b5ed2d4e10ae5223cd64e92454","keyword":["2012","BLM","Boundaries","Bureau of Land Management","DOI","Management","Montana","NWP 2012","North Dakota","Northwestern Plains","REA","Rapid Ecoregional Assessment","Report","South Dakota","Wyoming","environment","planning"],"last_harvested_date":"2026-10-07T21:36:04.672312","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":3,"publisher":"Bureau of Land Management","slug":"blm-natl-rea-northwestern-plains-rapid-ecoregional-assessment-rea-appendices-f-g-and--2012","spatial_centroid":{"lat":37.05068,"lon":-107.75686},"spatial_shape":{"coordinates":[[[-124.4783,29.0842],[-124.4783,49.0004],[-82.6747,49.0004],[-82.6747,29.0842],[-124.4783,29.0842]]],"type":"Polygon"},"theme":["non-geospatial"],"title":"BLM Natl REA Northwestern Plains Rapid Ecoregional Assessment (REA) Appendices F - G and H Data Quality Evaluation - Ecological Intactness and NatureServe Climate Change Vulnerability Index (NSCCVI) Reports - December 2012","type":"dataset"},{"_score":33.664024,"_sort":[1791408960241,33.664024,3,"b2740b9a-1015-4e8d-b847-08fafd6ff99c"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:04"],"contactPoint":{"@type":"vcard:Contact","fn":"Bureau of Land Management, BLM_OC_REA_Data_Portal_Feedback_Team","hasEmail":"mailto:BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov"},"description":"<div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>The Rapid Ecoregional Assessments (REAs) were launched in 2010 to help improve the understanding of existing condition for ecoregions and how conditions may be altered by ongoing environmental changes and land use. They do not allocate resource uses or make management decisions. They provide science-based information and tools for land managers and stakeholders to consider in subsequent resource planning and decision making processes. REAs examine ecological values, conditions, and trends within large, connected areas that have similar environmental characteristics in order to capitalize on efficiency of scale. REAs provide context to make site-specific decisions and conduct analyses for resource management plans and National Environmental Policy Act processes. The REAs seek to identify important resource values and patterns of environmental change that may not be evident when managing smaller, local land areas.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>This report contains Appendix H for the Middle Rockies Rapid Ecoregional Assessment providing NatureServe Climate Change Vulnerability Indices (NSCCVI) for the ecoregion.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>Contact: BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov</div>","distribution":[{"@type":"dcat:Distribution","accessURL":"https://blm-egis.maps.arcgis.com/sharing/rest/content/items/2dfdb7eaac6549d3b4dd20e794b7fedd/data","format":"PDF","mediaType":"application/pdf","title":"PDF Document"},{"@type":"dcat:Distribution","accessURL":"https://blm-egis.maps.arcgis.com/sharing/rest/content/items/2dfdb7eaac6549d3b4dd20e794b7fedd/info/metadata/metadata.xml?format=iso19139","mediaType":"text/xml","title":"ISO-19139 metadata"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/documents/BLM-EGIS::blm-natl-rea-middle-rockies-rapid-ecoregional-assessment-rea-appendix-h-natureserve-climate-change-vulnerability-indices-december-2012","format":"Web Page","mediaType":"text/html","title":"ArcGIS Hub Dataset"}],"identifier":"https://blm-egis.maps.arcgis.com/home/item.html?id=2dfdb7eaac6549d3b4dd20e794b7fedd","issued":"2022-10-28T20:32:39Z","keyword":["2012","BLM","Boundaries","Bureau of Land Management","DOI","Idaho","MIR 2012","Management","Middle Rockies","Montana","REA","Rapid Ecoregional Assessment","Report","Wyoming","environment","planning"],"landingPage":"https://gbp-blm-egis.hub.arcgis.com/documents/BLM-EGIS::blm-natl-rea-middle-rockies-rapid-ecoregional-assessment-rea-appendix-h-natureserve-climate-change-vulnerability-indices-december-2012","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2024-06-22T08:31:00Z","programCode":["010:000"],"publisher":{"@type":"org:Organization","name":"Bureau of Land Management"},"spatial":"-124.4783,29.0842,-82.6747,49.0004","theme":["non-geospatial"],"title":"BLM Natl REA Middle Rockies Rapid Ecoregional Assessment (REA) Appendix H NatureServe Climate Change Vulnerability Indices - December 2012"},"description":"<div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>The Rapid Ecoregional Assessments (REAs) were launched in 2010 to help improve the understanding of existing condition for ecoregions and how conditions may be altered by ongoing environmental changes and land use. They do not allocate resource uses or make management decisions. They provide science-based information and tools for land managers and stakeholders to consider in subsequent resource planning and decision making processes. REAs examine ecological values, conditions, and trends within large, connected areas that have similar environmental characteristics in order to capitalize on efficiency of scale. REAs provide context to make site-specific decisions and conduct analyses for resource management plans and National Environmental Policy Act processes. The REAs seek to identify important resource values and patterns of environmental change that may not be evident when managing smaller, local land areas.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>This report contains Appendix H for the Middle Rockies Rapid Ecoregional Assessment providing NatureServe Climate Change Vulnerability Indices (NSCCVI) for the ecoregion.</div><div style='color:rgb(0, 0, 0); font-family:Calibri; font-size:14.6667px;'>Contact: BLM_OC_REA_Data_Portal_Feedback_Team@blm.gov</div>","distribution_titles":["PDF Document","ISO-19139 metadata","ArcGIS Hub Dataset"],"harvest_record":"https://catalog.data.gov/harvest_record/84ccd37f-4f11-461e-9248-4a2125ce3a21","harvest_record_raw":"https://catalog.data.gov/harvest_record/84ccd37f-4f11-461e-9248-4a2125ce3a21/raw","has_download":false,"has_spatial":true,"identifier":"https://blm-egis.maps.arcgis.com/home/item.html?id=2dfdb7eaac6549d3b4dd20e794b7fedd","keyword":["2012","BLM","Boundaries","Bureau of Land Management","DOI","Idaho","MIR 2012","Management","Middle Rockies","Montana","REA","Rapid Ecoregional Assessment","Report","Wyoming","environment","planning"],"last_harvested_date":"2026-10-07T21:36:00.241601","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":3,"publisher":"Bureau of Land Management","slug":"blm-natl-rea-middle-rockies-rapid-ecoregional-assessment-rea-appendix-h-natureserve-c-2012","spatial_centroid":{"lat":37.05068,"lon":-107.75686},"spatial_shape":{"coordinates":[[[-124.4783,29.0842],[-124.4783,49.0004],[-82.6747,49.0004],[-82.6747,29.0842],[-124.4783,29.0842]]],"type":"Polygon"},"theme":["non-geospatial"],"title":"BLM Natl REA Middle Rockies Rapid Ecoregional Assessment (REA) Appendix H NatureServe Climate Change Vulnerability Indices - December 2012","type":"dataset"},{"_score":8.195236,"_sort":[1791399483440,8.195236,3,"374f22e6-71ff-41c8-ad59-226d7c041e6e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the  <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s  <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the  <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/vc6r-v7av","issued":"2024-03-28","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"landingPage":"https://data.sf.gov/d/vc6r-v7av","modified":"2026-10-02","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory"},"description":"<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the  <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s  <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the  <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/2298039f-dbac-4d77-9148-ba34fe56d537","harvest_record_raw":"https://catalog.data.gov/harvest_record/2298039f-dbac-4d77-9148-ba34fe56d537/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/vc6r-v7av","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"last_harvested_date":"2026-10-07T18:58:03.440545","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":3,"publisher":"data.sf.gov","slug":"san-francisco-municipal-natural-gas-equipment-inventory","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory","type":"dataset"},{"_score":26.588577,"_sort":[1791399453705,26.588577,3,"10888954-a747-4e51-a777-34b34c453c43"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"SFDPH Open Data","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The San Francisco Department of Public Health Flood Health Vulnerability Index is a composite index that measures the spatial distribution and relative vulnerability of San Francisco communities to the health impacts of flood inundation and extreme storms. The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the \"An Assessment of San Francisco\u2019s Vulnerability to Flooding & Extreme Storms\" located at <a href=\"https://www.sf.gov/sites/default/files/2023-05/FloodVulnerabilityReport_v5.pdf.pdf\">here</a>.\n\nData dictionary can be found in the attachments section of the metadata.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/cne3-h93g/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/cne3-h93g/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/cne3-h93g","issued":"2016-03-25","keyword":["@sfclimatehealth.org","climate change","community resiliency","dph","flood","health assessment","health impacts","public health","san francisco climate and health program","sea level rise","sfclimatehealth.org"],"landingPage":"https://data.sf.gov/d/cne3-h93g","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2026-10-02","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Health and Social Services"],"title":"San Francisco Flood Health Vulnerability"},"description":"The San Francisco Department of Public Health Flood Health Vulnerability Index is a composite index that measures the spatial distribution and relative vulnerability of San Francisco communities to the health impacts of flood inundation and extreme storms. The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the \"An Assessment of San Francisco\u2019s Vulnerability to Flooding & Extreme Storms\" located at <a href=\"https://www.sf.gov/sites/default/files/2023-05/FloodVulnerabilityReport_v5.pdf.pdf\">here</a>.\n\nData dictionary can be found in the attachments section of the metadata.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/7e036fdb-ad13-4b56-8056-67cb7446c9af","harvest_record_raw":"https://catalog.data.gov/harvest_record/7e036fdb-ad13-4b56-8056-67cb7446c9af/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/cne3-h93g","keyword":["@sfclimatehealth.org","climate change","community resiliency","dph","flood","health assessment","health impacts","public health","san francisco climate and health program","sea level rise","sfclimatehealth.org"],"last_harvested_date":"2026-10-07T18:57:33.705316","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":3,"publisher":"data.sf.gov","slug":"san-francisco-flood-health-vulnerability","spatial_centroid":null,"spatial_shape":null,"theme":["Health and Social Services"],"title":"San Francisco Flood Health Vulnerability","type":"dataset"},{"_score":9.905285,"_sort":[1791399372323,9.905285,1,"3df876e7-53ee-4eab-805a-0d59702a1af2"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NY Open Data","hasEmail":"mailto:no-reply@data.ny.gov"},"description":"The Division of Building Standards and Codes (BSC) administers the mandatory statewide Uniform Fire Prevention and Building Code (Uniform Code) and State Energy Conservation Construction Code (Energy Code). The Division provides technical assistance, administers variances, delivers educational courses, oversees the enforcement practices of local governments and serves as secretariat to the State Fire Prevention and Building Code Council. The Division program was created by Chapter 707 of the Laws of 1981. The New York Legislature enacted Article 18 of the Executive Law, directing the formulation of a Uniform Fire Prevention and Building Code (Uniform Code). The Uniform Code is designed to cover new construction, building rehabilitation, fire safety, and housing maintenance.\n\nThis dataset provides annually reported data on administration and enforcement of the State Energy Conservation Construction Code in each local government jurisdiction pursuant to 19 NYCRR 1203.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/vfqs-vuqv/columns.json","describedByType":"application/json","downloadURL":"https://data.ny.gov/api/v3/views/vfqs-vuqv/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/vfqs-vuqv/columns.xml","describedByType":"application/xml","downloadURL":"https://data.ny.gov/api/v3/views/vfqs-vuqv/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/vfqs-vuqv/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.ny.gov/api/views/vfqs-vuqv","issued":"2020-10-27","keyword":["1203","bsi","building permits","ceo","climate","code","code enforcement","codebuilding","conservation","economy","efficiency","energy"],"landingPage":"https://data.ny.gov/d/vfqs-vuqv","modified":"2026-10-01","publisher":{"@type":"org:Organization","name":"data.ny.gov"},"theme":["Government & Finance"],"title":"Energy Code Annual Report Submissions: Beginning 2007"},"description":"The Division of Building Standards and Codes (BSC) administers the mandatory statewide Uniform Fire Prevention and Building Code (Uniform Code) and State Energy Conservation Construction Code (Energy Code). The Division provides technical assistance, administers variances, delivers educational courses, oversees the enforcement practices of local governments and serves as secretariat to the State Fire Prevention and Building Code Council. The Division program was created by Chapter 707 of the Laws of 1981. The New York Legislature enacted Article 18 of the Executive Law, directing the formulation of a Uniform Fire Prevention and Building Code (Uniform Code). The Uniform Code is designed to cover new construction, building rehabilitation, fire safety, and housing maintenance.\n\nThis dataset provides annually reported data on administration and enforcement of the State Energy Conservation Construction Code in each local government jurisdiction pursuant to 19 NYCRR 1203.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/036884ba-950a-474b-9ec4-7037ae2c6ebf","harvest_record_raw":"https://catalog.data.gov/harvest_record/036884ba-950a-474b-9ec4-7037ae2c6ebf/raw","has_download":true,"has_spatial":false,"identifier":"https://data.ny.gov/api/views/vfqs-vuqv","keyword":["1203","bsi","building permits","ceo","climate","code","code enforcement","codebuilding","conservation","economy","efficiency","energy"],"last_harvested_date":"2026-10-07T18:56:12.323279","organization":{"aliases":["ny"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"dde3fc99-e074-41cf-a843-14aa9c777889","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png","name":"State of New York","organization_type":"State Government","slug":"new-york"},"parent_identifier":null,"popularity":1,"publisher":"data.ny.gov","slug":"energy-code-annual-report-submissions-beginning-2007","spatial_centroid":null,"spatial_shape":null,"theme":["Government & Finance"],"title":"Energy Code Annual Report Submissions: Beginning 2007","type":"dataset"},{"_score":11.489072,"_sort":[1791399364280,11.489072,4,"a5fbbe1f-4479-45ca-9837-2c8bee90dead"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NY Open Data","hasEmail":"mailto:no-reply@data.ny.gov"},"description":"This dataset identifies areas throughout the State that meet the interim criteria identified for a disadvantaged community as defined by New York State. It contains the 4,145 Census block groups that make up the current interim Disadvantaged Communities (DAC) approach. NYSERDA currently hosts a map on its website showing the interim DAC areas with an address look-up function that allows users to enter an address to determine if the address falls inside or outside an interim DAC: https://www.nyserda.ny.gov/ny/disadvantaged-communities\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/t6wd-tdrv/columns.json","describedByType":"application/json","downloadURL":"https://data.ny.gov/api/v3/views/t6wd-tdrv/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/t6wd-tdrv/columns.xml","describedByType":"application/xml","downloadURL":"https://data.ny.gov/api/v3/views/t6wd-tdrv/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/t6wd-tdrv/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/t6wd-tdrv/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/t6wd-tdrv/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/t6wd-tdrv/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.ny.gov/api/views/t6wd-tdrv","issued":"2021-01-22","keyword":["cjwg","clcpa","climate justice working group","climate leadership and community protection act","dac","disadvantaged communities","ej","environmental justice","lmi","low to moderate income"],"landingPage":"https://data.ny.gov/d/t6wd-tdrv","modified":"2026-10-01","publisher":{"@type":"org:Organization","name":"data.ny.gov"},"theme":["Energy & Environment"],"title":"Interim Disadvantaged Communities (DAC): 2020"},"description":"This dataset identifies areas throughout the State that meet the interim criteria identified for a disadvantaged community as defined by New York State. It contains the 4,145 Census block groups that make up the current interim Disadvantaged Communities (DAC) approach. NYSERDA currently hosts a map on its website showing the interim DAC areas with an address look-up function that allows users to enter an address to determine if the address falls inside or outside an interim DAC: https://www.nyserda.ny.gov/ny/disadvantaged-communities\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/4ad7471f-f0b3-4770-97e3-ff3f175493b8","harvest_record_raw":"https://catalog.data.gov/harvest_record/4ad7471f-f0b3-4770-97e3-ff3f175493b8/raw","has_download":true,"has_spatial":false,"identifier":"https://data.ny.gov/api/views/t6wd-tdrv","keyword":["cjwg","clcpa","climate justice working group","climate leadership and community protection act","dac","disadvantaged communities","ej","environmental justice","lmi","low to moderate income"],"last_harvested_date":"2026-10-07T18:56:04.280761","organization":{"aliases":["ny"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"dde3fc99-e074-41cf-a843-14aa9c777889","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png","name":"State of New York","organization_type":"State Government","slug":"new-york"},"parent_identifier":null,"popularity":4,"publisher":"data.ny.gov","slug":"interim-disadvantaged-communities-dac-2020","spatial_centroid":null,"spatial_shape":null,"theme":["Energy & Environment"],"title":"Interim Disadvantaged Communities (DAC): 2020","type":"dataset"},{"_score":79.27151,"_sort":[1791399351638,79.27151,32,"1707633b-78a4-449c-91f3-b74b0cf3c0cb"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NY Open Data","hasEmail":"mailto:no-reply@data.ny.gov"},"description":"The preferred citation when using this dataset is: Stevens, A., & Lamie, C., Eds. (2024). New York State Climate Impacts Assessment: Understanding and preparing for our changing climate. The New York State Climate Impacts Assessment is an investigation into how climate change will affect New York State\u2019s communities, ecosystems, and economy. The data and information presented will help New Yorkers plan and prepare for the impacts of climate change. The assessment also strives to show how addressing climate change provides opportunities to enhance equity and reduce the vulnerability of those most at risk.\n\nAs part of the assessment, Columbia University developed climate change projections for temperature and precipitation, extreme events, degree days, and sea level rise, downscaled to 12 regions of New York State. This dataset includes those projections of future climate conditions in New York State, for the 2030s through 2100.\n\nFor more information on these projections or to read the full NYS Climate Impacts Assessment, visit the assessment website at https://nysclimateimpacts.org/. \n\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/necg-zeuh/columns.json","describedByType":"application/json","downloadURL":"https://data.ny.gov/api/v3/views/necg-zeuh/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/necg-zeuh/columns.xml","describedByType":"application/xml","downloadURL":"https://data.ny.gov/api/v3/views/necg-zeuh/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/necg-zeuh/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.ny.gov/api/views/necg-zeuh","issued":"2024-09-24","keyword":["climate change","climate change assessment","climate impacts assessment","climate projections","degree days","extreme events","extreme heat","extreme precipitation","precipitation","sea level","sea level rise","temperature"],"landingPage":"https://data.ny.gov/d/necg-zeuh","modified":"2026-10-01","publisher":{"@type":"org:Organization","name":"data.ny.gov"},"theme":["Energy & Environment"],"title":"NYS Climate Impacts Assessment: Climate Change Projections"},"description":"The preferred citation when using this dataset is: Stevens, A., & Lamie, C., Eds. (2024). New York State Climate Impacts Assessment: Understanding and preparing for our changing climate. The New York State Climate Impacts Assessment is an investigation into how climate change will affect New York State\u2019s communities, ecosystems, and economy. The data and information presented will help New Yorkers plan and prepare for the impacts of climate change. The assessment also strives to show how addressing climate change provides opportunities to enhance equity and reduce the vulnerability of those most at risk.\n\nAs part of the assessment, Columbia University developed climate change projections for temperature and precipitation, extreme events, degree days, and sea level rise, downscaled to 12 regions of New York State. This dataset includes those projections of future climate conditions in New York State, for the 2030s through 2100.\n\nFor more information on these projections or to read the full NYS Climate Impacts Assessment, visit the assessment website at https://nysclimateimpacts.org/. \n\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/df03b3bb-c536-4281-b98b-2cc30f433357","harvest_record_raw":"https://catalog.data.gov/harvest_record/df03b3bb-c536-4281-b98b-2cc30f433357/raw","has_download":true,"has_spatial":false,"identifier":"https://data.ny.gov/api/views/necg-zeuh","keyword":["climate change","climate change assessment","climate impacts assessment","climate projections","degree days","extreme events","extreme heat","extreme precipitation","precipitation","sea level","sea level rise","temperature"],"last_harvested_date":"2026-10-07T18:55:51.638890","organization":{"aliases":["ny"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"dde3fc99-e074-41cf-a843-14aa9c777889","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png","name":"State of New York","organization_type":"State Government","slug":"new-york"},"parent_identifier":null,"popularity":32,"publisher":"data.ny.gov","slug":"nys-climate-impacts-assessment-climate-change-projections","spatial_centroid":null,"spatial_shape":null,"theme":["Energy & Environment"],"title":"NYS Climate Impacts Assessment: Climate Change Projections","type":"dataset"},{"_score":11.716312,"_sort":[1791399338812,11.716312,7,"a10e5023-e04a-489e-bb3e-1a02043b2958"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NY Open Data","hasEmail":"mailto:no-reply@data.ny.gov"},"description":"Energy storage is critical to New York\u2019s clean energy future. As renewable power sources like wind and solar provide a larger portion of New York\u2019s electricity, storage will allow clean energy to be available when and where it is most needed.  The 2019 Climate Act set a statewide goal of 3,000 MW of Energy Storage by 2030, further increased to 6,000 MW of Energy Storage by 2030 by Governor Kathy Hochul.  \n\nThis dataset tracks progress towards these statewide goals by compiling data on installed energy storage projects. Projects that received funding support from NYSERDA, as well as unincentivized projects, are included in this dataset\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help develop energy storage projects. Please see https://www.nyserda.ny.gov/All-Programs/Energy-Storage-Program.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/hspb-4n4p/columns.json","describedByType":"application/json","downloadURL":"https://data.ny.gov/api/v3/views/hspb-4n4p/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/hspb-4n4p/columns.xml","describedByType":"application/xml","downloadURL":"https://data.ny.gov/api/v3/views/hspb-4n4p/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/hspb-4n4p/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/hspb-4n4p/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/hspb-4n4p/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/hspb-4n4p/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.ny.gov/api/views/hspb-4n4p","issued":"2023-04-05","keyword":["energy storage","environment","mw","mwh","renewable"],"landingPage":"https://data.ny.gov/d/hspb-4n4p","modified":"2026-10-02","publisher":{"@type":"org:Organization","name":"data.ny.gov"},"theme":["Energy & Environment"],"title":"All Statewide Energy Storage Projects"},"description":"Energy storage is critical to New York\u2019s clean energy future. As renewable power sources like wind and solar provide a larger portion of New York\u2019s electricity, storage will allow clean energy to be available when and where it is most needed.  The 2019 Climate Act set a statewide goal of 3,000 MW of Energy Storage by 2030, further increased to 6,000 MW of Energy Storage by 2030 by Governor Kathy Hochul.  \n\nThis dataset tracks progress towards these statewide goals by compiling data on installed energy storage projects. Projects that received funding support from NYSERDA, as well as unincentivized projects, are included in this dataset\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help develop energy storage projects. Please see https://www.nyserda.ny.gov/All-Programs/Energy-Storage-Program.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/546749e6-51bf-42d7-a85b-72bb0634fcd6","harvest_record_raw":"https://catalog.data.gov/harvest_record/546749e6-51bf-42d7-a85b-72bb0634fcd6/raw","has_download":true,"has_spatial":false,"identifier":"https://data.ny.gov/api/views/hspb-4n4p","keyword":["energy storage","environment","mw","mwh","renewable"],"last_harvested_date":"2026-10-07T18:55:38.812616","organization":{"aliases":["ny"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"dde3fc99-e074-41cf-a843-14aa9c777889","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png","name":"State of New York","organization_type":"State Government","slug":"new-york"},"parent_identifier":null,"popularity":7,"publisher":"data.ny.gov","slug":"all-statewide-energy-storage-projects","spatial_centroid":null,"spatial_shape":null,"theme":["Energy & Environment"],"title":"All Statewide Energy Storage Projects","type":"dataset"},{"_score":6.4004307,"_sort":[1791399303686,6.4004307,1,"696461d9-16ea-45e9-8730-4e0a74e9feb7"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NY Open Data","hasEmail":"mailto:no-reply@data.ny.gov"},"description":"IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR\u00ae Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases\n\nD I S C L A I M E R: \nNormalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter.\n\nHome Performance with ENERGY STAR\u00ae Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA\u2019s website at:  http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. \n\nThis dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/5vqm-4rpf/columns.json","describedByType":"application/json","downloadURL":"https://data.ny.gov/api/v3/views/5vqm-4rpf/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.ny.gov/api/views/5vqm-4rpf/columns.xml","describedByType":"application/xml","downloadURL":"https://data.ny.gov/api/v3/views/5vqm-4rpf/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/5vqm-4rpf/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/5vqm-4rpf/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/5vqm-4rpf/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.ny.gov/api/v3/views/5vqm-4rpf/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.ny.gov/api/views/5vqm-4rpf","issued":"2019-02-12","keyword":["baseline","consumption","energy efficiency","energy savings","evaluated","home performance with energy star","hpwes","kwh","meter","mmbtu","p4p","pay for performance","residential","savings measurement","usage","weather normalized"],"landingPage":"https://data.ny.gov/d/5vqm-4rpf","modified":"2026-10-01","publisher":{"@type":"org:Organization","name":"data.ny.gov"},"theme":["Energy & Environment"],"title":"Residential Existing Homes (One to Four Units) Energy Efficiency Meter Evaluated Project Data: 2007 \u2013 2012"},"description":"IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR\u00ae Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases\n\nD I S C L A I M E R: \nNormalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter.\n\nHome Performance with ENERGY STAR\u00ae Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA\u2019s website at:  http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. \n\nThis dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA\u2019s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/7bc3299b-a486-450a-bb54-26da1f25b3e3","harvest_record_raw":"https://catalog.data.gov/harvest_record/7bc3299b-a486-450a-bb54-26da1f25b3e3/raw","has_download":true,"has_spatial":false,"identifier":"https://data.ny.gov/api/views/5vqm-4rpf","keyword":["baseline","consumption","energy efficiency","energy savings","evaluated","home performance with energy star","hpwes","kwh","meter","mmbtu","p4p","pay for performance","residential","savings measurement","usage","weather normalized"],"last_harvested_date":"2026-10-07T18:55:03.686747","organization":{"aliases":["ny"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"dde3fc99-e074-41cf-a843-14aa9c777889","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png","name":"State of New York","organization_type":"State Government","slug":"new-york"},"parent_identifier":null,"popularity":1,"publisher":"data.ny.gov","slug":"residential-existing-homes-one-to-four-units-energy-efficiency-meter-evaluated-p-2007-2012","spatial_centroid":null,"spatial_shape":null,"theme":["Energy & Environment"],"title":"Residential Existing Homes (One to Four Units) Energy Efficiency Meter Evaluated Project Data: 2007 \u2013 2012","type":"dataset"},{"_score":8.552271,"_sort":[1791335038027,8.552271,1,"e3db3d13-7407-4fe1-8ba0-18aaf07daf7f"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"GREG STENSAAS","hasEmail":"mailto:stensaas@usgs.gov"},"description":"On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nMauritania 1 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.","distribution":[{"@type":"dcat:Distribution","description":"NASA Ocean Color Web - Algorithm Description Documentation","downloadURL":"https://oceancolor.gsfc.nasa.gov/resources/atbd/","format":"HTML","mediaType":"text/html","title":"View this dataset's algorithm theoretical basis document"},{"@type":"dcat:Distribution","description":"NASA Ocean Color Web - Data Citation Guidelines","downloadURL":"https://oceancolor.gsfc.nasa.gov/resources/how-to-cite/","format":"HTML","mediaType":"text/html","title":"View information related to this dataset"},{"@type":"dcat:Distribution","description":"NASA Ocean Color Web - Data Distribution Site","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-2/SNPP-VIIRS/","format":"HTML","mediaType":"text/html","title":"Download this dataset through a directory map"},{"@type":"dcat:Distribution","description":"NASA Ocean Color Web - Processing History","downloadURL":"https://oceancolor.gsfc.nasa.gov/data/reprocessing/","format":"HTML","mediaType":"text/html","title":"View this dataset's processing history"}],"identifier":"C1220566922-USGS_LTA","issued":"1972-09-26","keyword":["earth-science","land-surface","land-use-land-cover","national-geospatial-data-asset","ngda","sensor-characteristics","spectral-engineering","surface-radiative-properties","surface-thermal-properties"],"landingPage":"https://cmr.earthdata.nasa.gov:443/search/concepts/C1220566922-USGS_LTA.html","language":["en-US"],"modified":"2025-03-31","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"DOI/USGS/EROS"},"spatial":"-10.74 17.74 -7.9 21.26","temporal":"1972-09-26T00:00:00Z/2023-02-28T00:00:00Z","theme":["CWIC","geospatial"],"title":"CEOS Cal Val Test Site - Mauritania 1 - Pseudo-Invariant Calibration Site (PICS)"},"description":"On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nPseudo-Invariant Calibration Sites (PICS):\nMauritania 1 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.","distribution_titles":["View this dataset's algorithm theoretical basis document","View information related to this dataset","Download this dataset through a directory map","View this dataset's processing history"],"harvest_record":"https://catalog.data.gov/harvest_record/84ed7ee5-acc3-4e3e-a372-10fb7e42dfaa","harvest_record_raw":"https://catalog.data.gov/harvest_record/84ed7ee5-acc3-4e3e-a372-10fb7e42dfaa/raw","has_download":true,"has_spatial":true,"identifier":"C1220566922-USGS_LTA","keyword":["earth-science","land-surface","land-use-land-cover","national-geospatial-data-asset","ngda","sensor-characteristics","spectral-engineering","surface-radiative-properties","surface-thermal-properties"],"last_harvested_date":"2026-10-07T01:03:58.027163","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":"DOI/USGS/EROS","slug":"ceos-cal-val-test-site-mauritania-1-pseudo-invariant-calibration-site-pics","spatial_centroid":null,"spatial_shape":null,"theme":["CWIC","geospatial"],"title":"CEOS Cal Val Test Site - Mauritania 1 - Pseudo-Invariant Calibration Site (PICS)","type":"dataset"},{"_score":8.883162,"_sort":[1791334889357,8.883162,2,"283777eb-31ff-4bd9-834e-6bcb0368ab5e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"GREG STENSAAS","hasEmail":"mailto:stensaas@usgs.gov"},"description":"On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\r\n\r\nBackground:\r\n\r\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\r\nRequirement:\r\n\r\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\r\n\r\nInstrumented Sites:\r\nDome C, Antarctica is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.","distribution":[{"@type":"dcat:Distribution","description":"Collection-specific granule Open Search Descriptor Document","downloadURL":"https://cmr.earthdata.nasa.gov/opensearch/granules/descriptor_document.xml?collectionConceptId=C1220566821-USGS_LTA","format":"application/opensearchdescription+xml","mediaType":"application/opensearchdescription+xml","title":"Retrieve the OpenSearch Get Capabilities document"},{"@type":"dcat:Distribution","description":"Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Test Sites.","downloadURL":"http://calvalportal.ceos.org/web/guest/home","format":"HTML","mediaType":"text/html","title":"The dataset's project home page"}],"identifier":"C1220566821-USGS_LTA","issued":"1972-12-06","keyword":["earth-science","land-surface","land-use-land-cover","national-geospatial-data-asset","ngda","sensor-characteristics","spectral-engineering","surface-radiative-properties","surface-thermal-properties"],"landingPage":"https://cmr.earthdata.nasa.gov:443/search/concepts/C1220566821-USGS_LTA.html","language":["en-US"],"modified":"2025-03-31","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"DOI/USGS/EROS"},"spatial":"123.0 -76.6 131.18 -74.5","temporal":"1972-12-06T00:00:00Z/2023-02-28T00:00:00Z","theme":["CWIC","geospatial"],"title":"CEOS Cal Val Test Site - Dome C, Antarctica - Instrumented Site"},"description":"On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\r\n\r\nBackground:\r\n\r\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\r\nRequirement:\r\n\r\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\r\n\r\nInstrumented Sites:\r\nDome C, Antarctica is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.\u00a0 In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.","distribution_titles":["Retrieve the OpenSearch Get Capabilities document","The dataset's project home page"],"harvest_record":"https://catalog.data.gov/harvest_record/e859b6dd-d3a9-4b89-a80b-7cbafb4e4ca6","harvest_record_raw":"https://catalog.data.gov/harvest_record/e859b6dd-d3a9-4b89-a80b-7cbafb4e4ca6/raw","has_download":true,"has_spatial":true,"identifier":"C1220566821-USGS_LTA","keyword":["earth-science","land-surface","land-use-land-cover","national-geospatial-data-asset","ngda","sensor-characteristics","spectral-engineering","surface-radiative-properties","surface-thermal-properties"],"last_harvested_date":"2026-10-07T01:01:29.357231","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":"DOI/USGS/EROS","slug":"ceos-cal-val-test-site-dome-c-antarctica-instrumented-site","spatial_centroid":null,"spatial_shape":null,"theme":["CWIC","geospatial"],"title":"CEOS Cal Val Test Site - Dome C, Antarctica - Instrumented Site","type":"dataset"},{"_score":19.995441,"_sort":[1791334808954,19.995441,0,"f08c69e7-4085-4896-80ee-40027183f9a0"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"undefined","hasEmail":"mailto:support-asdc@earthdata.nasa.gov"},"description":"ATTREX-Aircraft_RemoteSensing_Temperature_Measurements are remotely sensed temperature profiles collected onboard the Global Hawk Unihabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of remotely sensed temperature profiles collected by the Microwave Temperature Profiler (MTP) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth\u2019s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedback. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA\u2019s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.","distribution":[{"@type":"dcat:Distribution","description":"ASDC Data and Information for ATTREX","downloadURL":"https://asdc.larc.nasa.gov/project/ATTREX","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"ASDC Direct Data Download for ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1","downloadURL":"https://asdc.larc.nasa.gov/data/ATTREX/Aircraft_RemoteSensing_Temperature_Measurements_1/","format":"HTML","mediaType":"text/html","title":"Download this dataset"},{"@type":"dcat:Distribution","description":"DOI data set landing page for ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1","downloadURL":"https://doi.org/10.5067/ASDC_DAAC/ATTREX/0001","format":"HTML","mediaType":"text/html","title":"This dataset's landing page"},{"@type":"dcat:Distribution","description":"ESPO Data Archive for ATTREX","downloadURL":"https://espoarchive.nasa.gov/archive/browse/attrex","format":"HTML","mediaType":"text/html","title":"The dataset's project home page"},{"@type":"dcat:Distribution","description":"ESPO home page for ATTREX","downloadURL":"https://espo.nasa.gov/attrex/","format":"HTML","mediaType":"text/html","title":"The dataset's project home page"},{"@type":"dcat:Distribution","description":"Earthdata Search for ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1 (NASA Application to search, discover, visualize, refine, and access NASA Earth Observation data)","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1536056465-LARC_ASDC","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","description":"How to cite ASDC data","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"HTML","mediaType":"text/html","title":"View this dataset's data citation policy"},{"@type":"dcat:Distribution","description":"Search results for publications that cite this dataset by its DOI.","downloadURL":"https://scholar.google.com/scholar?q=10.5067%2FASDC_DAAC%2FATTREX%2F0001","format":"HTML","mediaType":"text/html","title":"Google Scholar search results"}],"identifier":"C1536056465-LARC_ASDC","issued":"2015-01-30","keyword":["altitude","atmosphere","atmospheric-temperature","earth-science"],"landingPage":"https://doi.org/10.5067/ASDC_DAAC/ATTREX/0001","language":["en-US"],"modified":"2025-04-01","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"-135.62 -12.33 -113.17 36.12","temporal":"2011-10-01T00:00:00Z/2013-03-01T23:59:59Z","theme":["ATTREX","geospatial"],"title":"ATTREX-Aircraft_RemoteSensing_Temperature_Measurements"},"description":"ATTREX-Aircraft_RemoteSensing_Temperature_Measurements are remotely sensed temperature profiles collected onboard the Global Hawk Unihabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of remotely sensed temperature profiles collected by the Microwave Temperature Profiler (MTP) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth\u2019s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedback. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA\u2019s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.","distribution_titles":["View documentation related to this dataset","Download this dataset","This dataset's landing page","The dataset's project home page","The dataset's project home page","Download this dataset through Earthdata Search","View this dataset's data citation policy","Google Scholar search results"],"harvest_record":"https://catalog.data.gov/harvest_record/deb21670-d610-4f29-806d-472c1b269b3c","harvest_record_raw":"https://catalog.data.gov/harvest_record/deb21670-d610-4f29-806d-472c1b269b3c/raw","has_download":true,"has_spatial":true,"identifier":"C1536056465-LARC_ASDC","keyword":["altitude","atmosphere","atmospheric-temperature","earth-science"],"last_harvested_date":"2026-10-07T01:00:08.954561","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/LARC/SD/ASDC","slug":"attrex-aircraft_remotesensing_temperature_measurements","spatial_centroid":null,"spatial_shape":null,"theme":["ATTREX","geospatial"],"title":"ATTREX-Aircraft_RemoteSensing_Temperature_Measurements","type":"dataset"},{"_score":9.820318,"_sort":[1791334777464,9.820318,8,"c73d5fcc-f224-485b-a43f-32863b56307b"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Felix Landerer","hasEmail":"mailto:felix.w.landerer@jpl.nasa.gov"},"description":"Data from NASA's Grace satellites show that the land ice sheets in both Antarctica and Greenland are losing mass. The continent of Antarctica (left chart) has been losing more than 100 cubic kilometers (24 cubic miles) of ice per year since 2002.","distribution":[{"@type":"dcat:Distribution","description":"GeneLab Study Page","downloadURL":"https://genelab-data.ndc.nasa.gov/genelab/accession/GLDS-54","format":"HTML","mediaType":"text/html","title":"Immune responses to the in vitro LPS assault engineered in the spaceflight multi-omics study"}],"identifier":"NASA-0000043","issued":"2018-06-25","keyword":["apps","arctic-sea-ice","carbon-dioxide","climate","electricity","ephemerides","geodetics","land-ice","news","radar","waves"],"landingPage":"https://data.nasa.gov/dataset/land-ice-greenland-antarctic-ice-mass-anomaly","modified":"2025-04-23","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"National Aeronautics and Space Administration"},"spatial":"regional","temporal":"2002-01-01/2013-01-01","title":"Land Ice: Greenland & Antarctic ice mass anomaly"},"description":"Data from NASA's Grace satellites show that the land ice sheets in both Antarctica and Greenland are losing mass. The continent of Antarctica (left chart) has been losing more than 100 cubic kilometers (24 cubic miles) of ice per year since 2002.","distribution_titles":["Immune responses to the in vitro LPS assault engineered in the spaceflight multi-omics study"],"harvest_record":"https://catalog.data.gov/harvest_record/c1cca233-fcc5-4899-81fc-03f5f54c7a14","harvest_record_raw":"https://catalog.data.gov/harvest_record/c1cca233-fcc5-4899-81fc-03f5f54c7a14/raw","has_download":true,"has_spatial":true,"identifier":"NASA-0000043","keyword":["apps","arctic-sea-ice","carbon-dioxide","climate","electricity","ephemerides","geodetics","land-ice","news","radar","waves"],"last_harvested_date":"2026-10-07T00:59:37.464025","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":8,"publisher":"National Aeronautics and Space Administration","slug":"land-ice-greenland-antarctic-ice-mass-anomaly","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Land Ice: Greenland & Antarctic ice mass anomaly","type":"dataset"},{"_score":10.531846,"_sort":[1791334646504,10.531846,0,"a7a8b2b4-7477-4ea8-8e30-3ddf11eb615e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"NSIDC Services","hasEmail":"mailto:nsidc@nsidc.org"},"description":"This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 240 m Antarctic Ice Sheet extent masks at roughly annual resolution from 1997 through 2021. The ice masks were generated by combining data acquired by multiple satellite-borne optical, thermal, and radar sensors. The ice thickness and velocity data used to determine the presence of ice are also provided.","distribution":[{"@type":"dcat:Distribution","description":"Includes a user's guide, supplemental documents like ATBDs and academic papers, How Tos, FAQs, etc.","downloadURL":"https://doi.org/10.5067/9ZFX84T5GI6D","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.","downloadURL":"https://search.earthdata.nasa.gov/search?q=NSIDC-0794+V001","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","description":"Search and filter data files using a map-based interface","downloadURL":"https://nsidc.org/data/data-access-tool/NSIDC-0794/versions/1/","format":"HTML","mediaType":"text/html","title":"Download this dataset"},{"@type":"dcat:Distribution","description":"Search results for publications that cite this dataset by its DOI.","downloadURL":"https://scholar.google.com/scholar?q=10.5067%2F9ZFX84T5GI6D","format":"HTML","mediaType":"text/html","title":"Google Scholar search results"}],"identifier":"C3179071550-NSIDC_ECS","issued":"1997-10-01","keyword":["climate-indicators","cryosphere","cryospheric-indicators","earth-science","snow-ice"],"landingPage":"https://doi.org/10.5067/9ZFX84T5GI6D","language":["en-US"],"modified":"2025-07-17","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"NASA NSIDC DAAC"},"spatial":"-180.0 -90.0 180.0 -57.6","temporal":"1997-10-01T00:00:00Z/2021-03-14T23:59:59.999Z","theme":["geospatial"],"title":"MEaSUREs ITS_LIVE Antarctic Annual 240 m Ice Sheet Extent Masks, 1997-2021 V001"},"description":"This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 240 m Antarctic Ice Sheet extent masks at roughly annual resolution from 1997 through 2021. The ice masks were generated by combining data acquired by multiple satellite-borne optical, thermal, and radar sensors. The ice thickness and velocity data used to determine the presence of ice are also provided.","distribution_titles":["View documentation related to this dataset","Download this dataset through Earthdata Search","Download this dataset","Google Scholar search results"],"harvest_record":"https://catalog.data.gov/harvest_record/1e108bc9-d85c-45fd-af32-b912df1692db","harvest_record_raw":"https://catalog.data.gov/harvest_record/1e108bc9-d85c-45fd-af32-b912df1692db/raw","has_download":true,"has_spatial":true,"identifier":"C3179071550-NSIDC_ECS","keyword":["climate-indicators","cryosphere","cryospheric-indicators","earth-science","snow-ice"],"last_harvested_date":"2026-10-07T00:57:26.504339","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 NSIDC DAAC","slug":"measures-its_live-antarctic-annual-240-m-ice-sheet-extent-masks-1997-2021-v001","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"MEaSUREs ITS_LIVE Antarctic Annual 240 m Ice Sheet Extent Masks, 1997-2021 V001","type":"dataset"},{"_score":10.343893,"_sort":[1791334547462,10.343893,5,"d087edc7-0d01-49b0-bf61-19809db8c749"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"NSIDC Services","hasEmail":"mailto:nsidc@nsidc.org"},"description":"These data consist of soil moisture, soil temperature and precipitation measurements recorded in 2016 by the permanent soil moisture network; SMAPVEX16-Iowa. The sites were spread out over the experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA.\n\nThe data file contains the soil moisture, soil temperature and\u00a0precipitation measurements for each station located at the site.","distribution":[{"@type":"dcat:Distribution","description":"Data Access link for ITSD project","downloadURL":"https://nsidc.org/data/data-access-tool/SV16I_PNET/versions/1/","format":"HTML","mediaType":"text/html","title":"Download this dataset"},{"@type":"dcat:Distribution","description":"Includes a user's guide, supplemental documents like ATBDs and academic papers, How Tos, FAQs, etc.","downloadURL":"https://doi.org/10.5067/P3ZUS77FW7JL","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1990819364-NSIDC_ECS&m=37.93418934207473%21-110.109375%214%211%210%210%2C2&tl=1594227826%214","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","description":"Search results for publications that cite this dataset by its DOI.","downloadURL":"https://scholar.google.com/scholar?q=10.5067%2FP3ZUS77FW7JL","format":"HTML","mediaType":"text/html","title":"Google Scholar search results"}],"identifier":"C2011917772-NSIDC_ECS","issued":"2016-05-01","keyword":["atmospheric-ocean-indicators","climate-indicators","earth-science","land-surface-agriculture-indicators"],"landingPage":"https://doi.org/10.5067/P3ZUS77FW7JL","language":["en-US"],"modified":"2025-07-17","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"NASA NSIDC DAAC"},"spatial":"-94.0 42.33 -93.22 42.82","temporal":"2016-05-01T00:00:00Z/2016-09-30T23:59:59.999Z","theme":["geospatial"],"title":"SMAPVEX16 Iowa Permanent Soil Moisture Network V001"},"description":"These data consist of soil moisture, soil temperature and precipitation measurements recorded in 2016 by the permanent soil moisture network; SMAPVEX16-Iowa. The sites were spread out over the experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA.\n\nThe data file contains the soil moisture, soil temperature and\u00a0precipitation measurements for each station located at the site.","distribution_titles":["Download this dataset","View documentation related to this dataset","Download this dataset through Earthdata Search","Google Scholar search results"],"harvest_record":"https://catalog.data.gov/harvest_record/ab68b1ee-9207-48fd-b48e-aa5655e9e03b","harvest_record_raw":"https://catalog.data.gov/harvest_record/ab68b1ee-9207-48fd-b48e-aa5655e9e03b/raw","has_download":true,"has_spatial":true,"identifier":"C2011917772-NSIDC_ECS","keyword":["atmospheric-ocean-indicators","climate-indicators","earth-science","land-surface-agriculture-indicators"],"last_harvested_date":"2026-10-07T00:55:47.462312","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":"NASA NSIDC DAAC","slug":"smapvex16-iowa-permanent-soil-moisture-network-v001","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"SMAPVEX16 Iowa Permanent Soil Moisture Network V001","type":"dataset"},{"_score":19.483868,"_sort":[1791334224143,19.483868,1,"5b226a7f-facb-4271-9a3b-0357c5f15656"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"EUGENE FOSNIGHT","hasEmail":"mailto:fosnight@usgs.gov"},"description":"The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).","distribution":[{"@type":"dcat:Distribution","description":"Query and order satellite images, aerial photographs, and cartographic products through the U.S. Geological Survey. Log in as a guest or as a registered user. Registered users have access to more features than guests do. If you plan on using EarthExplorer frequently, you may wish to register. Please note that this site uses Session Cookies and Java applets.","downloadURL":"http://earthexplorer.usgs.gov","format":"HTML","mediaType":"text/html","title":"Download this dataset"}],"identifier":"C1220567235-USGS_LTA","issued":"1972-10-31","keyword":["agriculture","biosphere","earth-science","forest-science","habitat-conversion-fragmentation","human-dimensions","terrestrial-ecosystems","vegetation"],"landingPage":"https://cmr.earthdata.nasa.gov:443/search/concepts/C1220567235-USGS_LTA.html","language":["en-US"],"modified":"2025-07-17","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"DOI/USGS/EROS"},"spatial":"6.31 0.46 17.86 14.17","temporal":"1972-10-31T00:00:00Z/2022-01-17T00:00:00Z","theme":["CWIC","geospatial"],"title":"USGS Global Forest Observations Initiative (GFOI) Cameroon"},"description":"The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:\n\nfoster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).","distribution_titles":["Download this dataset"],"harvest_record":"https://catalog.data.gov/harvest_record/d617e5df-4788-421c-9f05-109b4a9e2adc","harvest_record_raw":"https://catalog.data.gov/harvest_record/d617e5df-4788-421c-9f05-109b4a9e2adc/raw","has_download":true,"has_spatial":true,"identifier":"C1220567235-USGS_LTA","keyword":["agriculture","biosphere","earth-science","forest-science","habitat-conversion-fragmentation","human-dimensions","terrestrial-ecosystems","vegetation"],"last_harvested_date":"2026-10-07T00:50:24.143962","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":"DOI/USGS/EROS","slug":"usgs-global-forest-observations-initiative-gfoi-cameroon","spatial_centroid":null,"spatial_shape":null,"theme":["CWIC","geospatial"],"title":"USGS Global Forest Observations Initiative (GFOI) Cameroon","type":"dataset"},{"_score":14.767541,"_sort":[1791334192216,14.767541,4,"4dc706d9-79ee-4c5a-a2f6-a720bd3b51f1"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Parnchai Sawaengphokhai","hasEmail":"mailto:parnchai.k.sawaengphokhai@nasa.gov"},"description":"FLASH_SSF_Terra-FM1-MODIS_Version4A is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in HDF Version 4A data product. This product consist of of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy Systems (CERES) Single Scanner Footprint (SSF) level for quick-look purposes. \r\n\r\nFLASHFlux data are a product line of the CERES project designed for processing and release of TOA and surface radiative fluxes for applied sciences and educations uses. The FLASHFlux data product is a rapid release product based upon the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecrafts. While of exceptional fidelity, these data products require a considerable amount of processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquisition of the initial measurements. For climate studies, such delays are of little consequence especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from the GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and clouds information. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997 as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.","identifier":"C1719289020-LARC_ASDC","issued":"2020-08-01","keyword":["atmosphere","atmospheric-radiation","clouds","earth-science"],"landingPage":"https://doi.org/10.5067/CERES/FLASH_SSF_Terra-FM1-MODIS_Version4A","language":["en-US"],"modified":"2025-07-17","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"<?xml version=\"1.0\" encoding=\"UTF-8\"?><gml:Polygon xmlns:gml=\"http://www.opengis.net/gml/3.2\" srsName=\"EPSG:9825\"><gml:outerBoundaryIs><gml:LinearRing><gml:posList>-90.0 -180.0 -90.0 180.0 90.0 180.0 90.0 -180.0 -90.0 -180.0</gml:posList></gml:LinearRing></gml:outerBoundaryIs><gml:innerBoundaryIs></gml:innerBoundaryIs></gml:Polygon>","temporal":"2019-01-01T00:00:00Z/2022-01-17T00:00:00Z","theme":["FLASHFLUX","geospatial"],"title":"Fast Longwave And SHortwave Fluxes (FLASHflux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in HDF Version 4A"},"description":"FLASH_SSF_Terra-FM1-MODIS_Version4A is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in HDF Version 4A data product. This product consist of of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy Systems (CERES) Single Scanner Footprint (SSF) level for quick-look purposes. \r\n\r\nFLASHFlux data are a product line of the CERES project designed for processing and release of TOA and surface radiative fluxes for applied sciences and educations uses. The FLASHFlux data product is a rapid release product based upon the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecrafts. While of exceptional fidelity, these data products require a considerable amount of processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquisition of the initial measurements. For climate studies, such delays are of little consequence especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. \r\n\r\nThe SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from the GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and clouds information. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997 as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/6b912d19-f4c6-4c21-9fda-791b66acfcdb","harvest_record_raw":"https://catalog.data.gov/harvest_record/6b912d19-f4c6-4c21-9fda-791b66acfcdb/raw","has_download":false,"has_spatial":true,"identifier":"C1719289020-LARC_ASDC","keyword":["atmosphere","atmospheric-radiation","clouds","earth-science"],"last_harvested_date":"2026-10-07T00:49:52.216571","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":"fast-longwave-and-shortwave-fluxes-flashflux-clouds-and-radiative-swath-ssf-terra-fm1-data","spatial_centroid":null,"spatial_shape":null,"theme":["FLASHFLUX","geospatial"],"title":"Fast Longwave And SHortwave Fluxes (FLASHflux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in HDF Version 4A","type":"dataset"},{"_score":46.225365,"_sort":[1791334112641,46.225365,7,"6a49b410-5383-4c1a-9d93-815f77b5f50b"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Nomana Intekhab Hadi","hasEmail":"mailto:nihadi_ruhee@berkeley.edu"},"description":"The objective of this work includes the coastal scenario, risks and development of coastal paleoclimatology through landscape mapping; by highlighting the devastating climate change impacts that might result in tsunami in South-East Asian sea basin with short or no-awareness period; despite the facts that the Southeast Asia region is generally poor being encompassed by twelve countries along with the Indian and Pacific Oceans. Special payable concern to the Bay of Bengal has been paid that can dictate region\u2019s climate to certain extent. The ecosystems\u2019 impact due to climate change and global warming -can bring direct variables and affects in \u2013salinity, temperature, river flow, runoff, soil characteristics, erosion, nutrition level and water quality. The landscape mapping can address the system infrastructure requirements to the SEAR\u2019s sea basin attainable by the annual monsoons, the Southwest and the Northeast Monsoons. How recent meteorological and geodynamic-genetic events can result in adverse economical damages and significant losses of lives are also drawn. This work monitors on Sea-level rise gets projected under global warming. The most brainstorming findings from climate change issues are how the high latitudes for SEAR Sea Basins\u2019 are likely to experience greater warming than the global mean and warming,- And how the hydrological cycle gets found responsible for bringing more floods and more droughts in- causing huge devastating changes for environmental factors in coastal zones","identifier":"DASHLINK_720","issued":"2013-05-08","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/720/","modified":"2025-07-17","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"South-East Asian Region (SEAR): Sea Basin Landscape Mapping for Paleoclimatology & Recent Climate Change Impacts"},"description":"The objective of this work includes the coastal scenario, risks and development of coastal paleoclimatology through landscape mapping; by highlighting the devastating climate change impacts that might result in tsunami in South-East Asian sea basin with short or no-awareness period; despite the facts that the Southeast Asia region is generally poor being encompassed by twelve countries along with the Indian and Pacific Oceans. Special payable concern to the Bay of Bengal has been paid that can dictate region\u2019s climate to certain extent. The ecosystems\u2019 impact due to climate change and global warming -can bring direct variables and affects in \u2013salinity, temperature, river flow, runoff, soil characteristics, erosion, nutrition level and water quality. The landscape mapping can address the system infrastructure requirements to the SEAR\u2019s sea basin attainable by the annual monsoons, the Southwest and the Northeast Monsoons. How recent meteorological and geodynamic-genetic events can result in adverse economical damages and significant losses of lives are also drawn. This work monitors on Sea-level rise gets projected under global warming. The most brainstorming findings from climate change issues are how the high latitudes for SEAR Sea Basins\u2019 are likely to experience greater warming than the global mean and warming,- And how the hydrological cycle gets found responsible for bringing more floods and more droughts in- causing huge devastating changes for environmental factors in coastal zones","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/8111be0b-4594-418c-87e4-1b170b35a2f9","harvest_record_raw":"https://catalog.data.gov/harvest_record/8111be0b-4594-418c-87e4-1b170b35a2f9/raw","has_download":false,"has_spatial":false,"identifier":"DASHLINK_720","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-10-07T00:48:32.641291","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":7,"publisher":"Dashlink","slug":"south-east-asian-region-sear-sea-basin-landscape-mapping-for-paleoclimatology-recent-clima","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"South-East Asian Region (SEAR): Sea Basin Landscape Mapping for Paleoclimatology & Recent Climate Change Impacts","type":"dataset"},{"_score":48.427933,"_sort":[1791334063771,48.427933,7,"4ddd6b8f-568e-4541-9540-7f348aa86bec"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"undefined","hasEmail":"mailto:metadata@ciesin.columbia.edu"},"description":"The Trends in Global Freshwater Availability from the Gravity Recovery and Climate Experiment (GRACE), 2002-2016, is a global gridded data set at a spatial resolution of 0.5 degrees that presents trends (rate of change measured in centimeters per year) in freshwater availability based on data obtained from 2002 to 2016 by NASA GRACE. Terrestrial water availability storage is the sum of groundwater, soil moisture, snow and ice, surface waters, and wet biomass, expressed as an equivalent height of water. GRACE measures changes in the terrestrial water cycle by assessing small changes in Earth's gravity field. This observation-based assessment of how the world's water cycle is responding to human impacts and climate variations provides an important tool for evaluating and predicting emerging threats to water and food security.","distribution":[{"@type":"dcat:Distribution","description":"Documentation Page","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/sdei-trends-freshwater-availability-grace/docs","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"Sample browse graphic of the data set.","downloadURL":"https://sedac.ciesin.columbia.edu/downloads/maps/sdei/sdei-trends-freshwater-availability-grace/sdei-trends-freshwater-availability-grace-global-2002-2016-thumbnail.jpg","format":"JPEG","mediaType":"image/jpeg","title":"Get a related visualization"},{"@type":"dcat:Distribution","description":"Search results for publications that cite this dataset by its DOI.","downloadURL":"https://scholar.google.com/scholar?q=10.7927%2FH4TT4P2C","format":"HTML","mediaType":"text/html","title":"Google Scholar search results"},{"@type":"dcat:Distribution","description":"Web Map Service Page","downloadURL":"https://sedac.ciesin.columbia.edu/data/set/sdei-trends-freshwater-availability-grace/maps/services","format":"HTML","mediaType":"text/html","title":"Use Web Map Service (WMS) to download the dataset's data"}],"identifier":"C1617586061-SEDAC","issued":"2019-05-22","keyword":["earth-science","ground-water","snow-ice","surface-water","terrestrial-hydrosphere"],"language":["en-US"],"modified":"2025-07-17","programCode":["026:001"],"publisher":{"@type":"org:Organization","name":"SEDAC"},"references":["https://doi.org/10.7927/h4q23xbz"],"spatial":"-180.0 -90.0 180.0 90.0","temporal":"2002-01-01T00:00:00Z/2016-12-31T00:00:00Z","theme":["SDEI","geospatial"],"title":"Trends in Global Freshwater Availability from the Gravity Recovery and Climate Experiment (GRACE)"},"description":"The Trends in Global Freshwater Availability from the Gravity Recovery and Climate Experiment (GRACE), 2002-2016, is a global gridded data set at a spatial resolution of 0.5 degrees that presents trends (rate of change measured in centimeters per year) in freshwater availability based on data obtained from 2002 to 2016 by NASA GRACE. Terrestrial water availability storage is the sum of groundwater, soil moisture, snow and ice, surface waters, and wet biomass, expressed as an equivalent height of water. GRACE measures changes in the terrestrial water cycle by assessing small changes in Earth's gravity field. This observation-based assessment of how the world's water cycle is responding to human impacts and climate variations provides an important tool for evaluating and predicting emerging threats to water and food security.","distribution_titles":["View documentation related to this dataset","Get a related visualization","Google Scholar search results","Use Web Map Service (WMS) to download the dataset's data"],"harvest_record":"https://catalog.data.gov/harvest_record/b2981f6b-68f7-4aab-bedd-0df825fd9fac","harvest_record_raw":"https://catalog.data.gov/harvest_record/b2981f6b-68f7-4aab-bedd-0df825fd9fac/raw","has_download":true,"has_spatial":true,"identifier":"C1617586061-SEDAC","keyword":["earth-science","ground-water","snow-ice","surface-water","terrestrial-hydrosphere"],"last_harvested_date":"2026-10-07T00:47:43.771371","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":7,"publisher":"SEDAC","slug":"trends-in-global-freshwater-availability-from-the-gravity-recovery-and-climate-experiment-","spatial_centroid":null,"spatial_shape":null,"theme":["SDEI","geospatial"],"title":"Trends in Global Freshwater Availability from the Gravity Recovery and Climate Experiment (GRACE)","type":"dataset"},{"_score":26.42017,"_sort":[1791333864548,26.42017,1,"3400b965-8b77-418e-99f2-e3d657e8883e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Thomas Morgan","hasEmail":"mailto:thomas.h.morgan@nasa.gov"},"description":"Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth\u2019s radiation budget and climate. It flies in formation with five other satellites in the international \u201cA-Train\u201d (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.","identifier":"NASA-0000157","issued":"2018-06-25","keyword":["aerosol","atmospheric-science","climate","cloud","eos","radiation","satellite"],"modified":"2025-07-17","programCode":["026:004"],"publisher":{"@type":"org:Organization","name":"National Aeronautics and Space Administration"},"temporal":"2007-03-01/2007-11-11","theme":["Earth Science"],"title":"CALIPSO Lidar L2 1/3 km Cloud Layer Data V1-20"},"description":"Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth\u2019s radiation budget and climate. It flies in formation with five other satellites in the international \u201cA-Train\u201d (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/e4895d38-0220-476f-979e-f62c93d07d46","harvest_record_raw":"https://catalog.data.gov/harvest_record/e4895d38-0220-476f-979e-f62c93d07d46/raw","has_download":false,"has_spatial":false,"identifier":"NASA-0000157","keyword":["aerosol","atmospheric-science","climate","cloud","eos","radiation","satellite"],"last_harvested_date":"2026-10-07T00:44:24.548699","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":"National Aeronautics and Space Administration","slug":"calipso-lidar-l2-1-3-km-cloud-layer-data-v1-20","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar L2 1/3 km Cloud Layer Data V1-20","type":"dataset"},{"_score":18.062685,"_sort":[1791333462317,18.062685,1,"7ebf5931-1b3a-402d-afab-ce628e5ed008"],"access_level":"public","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 daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model.  Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file 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/C2674700048-LPCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://npwbanalres.s3.us-west-2.amazonaws.com/index.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://screenedcleanedsummaries.s3.us-west-2.amazonaws.com/Gridded_Water_Balance_Model_April_2021.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2674700048-LPCLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C2674700048-LPCLOUD","keyword":["earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-precipitation-atmosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"DOI/NPS"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 55.457306, \"WestBoundingCoordinate\": -131.70607, \"EastBoundingCoordinate\": -60.530453, \"SouthBoundingCoordinate\": 21.115301}]]","temporal":"1980-01-01/2024-12-31","theme":["Earth Science"],"title":"Monthly Historical Water Balance Products for the CONUS"},"description":"This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model.  Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ae2be653-d270-42ed-803b-49eb27e887c9","harvest_record_raw":"https://catalog.data.gov/harvest_record/ae2be653-d270-42ed-803b-49eb27e887c9/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C2674700048-LPCLOUD","keyword":["earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-precipitation-atmosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-10-07T00:37:42.317253","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":"DOI/NPS","slug":"monthly-historical-water-balance-products-for-the-conus-c5498","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Monthly Historical Water Balance Products for the CONUS","type":"dataset"},{"_score":13.090958,"_sort":[1791333379301,13.090958,1,"7f600bfa-0597-4655-9920-b12860ed46a2"],"access_level":"public","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/C3166805774-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/NOAA20-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search?q=%2522VIIRSJ1_L2_SST%2522","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/NOAA-20","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C3166805774-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","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":"2017-11-29/2026-09-21","theme":["Earth Science"],"title":"NOAA-20 VIIRS Level-2 Regional  11\u00b5m Daytime Sea Surface Temperature (SST) Data, version 2024.0"},"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/1965e5a3-e5d6-4454-9726-224244ae051b","harvest_record_raw":"https://catalog.data.gov/harvest_record/1965e5a3-e5d6-4454-9726-224244ae051b/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C3166805774-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-10-07T00:36:19.301526","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":"noaa-20-viirs-level-regional-regional-11m-day-night-sea-surface-temperature-sst-data-versi-6969b","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"NOAA-20 VIIRS Level-2 Regional  11\u00b5m Daytime Sea Surface Temperature (SST) Data, version 2024.0","type":"dataset"},{"_score":14.971905,"_sort":[1791333378980,14.971905,1,"12896d4e-ae44-4692-8e32-97eb42fa66dc"],"access_level":"public","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/C3166805844-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/NOAA20-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search?q=%2522VIIRSJ1_L3b_SST%2522","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/NOAA-20","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C3166805844-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG"},"spatial":"[\"CARTESIAN\", [{\"SouthBoundingCoordinate\": -90, \"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180}]]","temporal":"2017-11-29/2026-09-21","theme":["Earth Science"],"title":"NOAA-20 VIIRS Level-3 Global Binned 11\u00b5m Daytime Sea Surface Temperature (SST) Data, version 2024.0"},"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- 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/87263ba3-2097-473e-b00e-7ca920ef457e","harvest_record_raw":"https://catalog.data.gov/harvest_record/87263ba3-2097-473e-b00e-7ca920ef457e/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C3166805844-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-10-07T00:36:18.980793","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":"noaa-20-viirs-level-3-global-binned-11m-daytime-sea-surface-temperature-sst-data-version-r-568ee","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"NOAA-20 VIIRS Level-3 Global Binned 11\u00b5m Daytime Sea Surface Temperature (SST) Data, version 2024.0","type":"dataset"},{"_score":12.968178,"_sort":[1791333378657,12.968178,3,"ba88537c-5ab0-4c6c-b609-06eb0e3e00a2"],"access_level":"public","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/C3166805868-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/NOAA20-VIIRS/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://oceandata.sci.gsfc.nasa.gov/opendap/VIIRSJ1/L3SMI/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search?q=%2522VIIRSJ1_L3m_SST_NRT%2522","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/NOAA-20","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C3166805868-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","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":"2017-11-29/2026-09-21","theme":["Earth Science"],"title":"NOAA-20 VIIRS Level-3 Global Mapped 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version 2024.0"},"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/77d9a4ec-9318-4597-a05f-afa35d9f900d","harvest_record_raw":"https://catalog.data.gov/harvest_record/77d9a4ec-9318-4597-a05f-afa35d9f900d/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C3166805868-OB_DAAC","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-10-07T00:36:18.657120","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/GCDC/OB.DAAC;OBPG","slug":"noaa-20-viirs-level-3-global-mapped-11m-day-night-sea-surface-temperature-sst-near-real-ti-ebcfa","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"NOAA-20 VIIRS Level-3 Global Mapped 11\u00b5m Datime Sea Surface Temperature(SST) - Near Real-time (NRT) Data, version 2024.0","type":"dataset"},{"_score":22.52362,"_sort":[1791333377484,22.52362,9,"e1c9ae26-8378-4786-8e29-fb6e2d2f85dd"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The NEX-GDDP dataset includes downscaled projections for RCP 4.5 and RCP 8.5 from the 21 models and scenarios for which daily scenarios were produced and distributed under CMIP5. Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the dataset is 0.25 degrees (~25 km x 25 km). The NEX-GDDP dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds.\r\n\r\nEach of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).","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/C1374483929-NCCS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://cds.nccs.nasa.gov/nex-gddp/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://dataserver.nccs.nasa.gov/thredds/catalog/bypass/NEX-GDDP/bcsd/catalog.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://nex.nasa.gov/nex/projects/1356/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://portal.nccs.nasa.gov/datashare/NEXGDDP/BCSD/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"/SDE/CMR_API/|C1374483929-NCCS","keyword":["earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/ARC/NAS/NEX;NASA/GSFC/SED/ESD/NCCS"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1950-01-01/2100-12-31","theme":["Earth Science"],"title":"NASA Earth Exchange Global Daily Downscaled Projections"},"description":"The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The NEX-GDDP dataset includes downscaled projections for RCP 4.5 and RCP 8.5 from the 21 models and scenarios for which daily scenarios were produced and distributed under CMIP5. Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the dataset is 0.25 degrees (~25 km x 25 km). The NEX-GDDP dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds.\r\n\r\nEach of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9f402a59-03fa-4b2f-ab5a-7824720cfeaf","harvest_record_raw":"https://catalog.data.gov/harvest_record/9f402a59-03fa-4b2f-ab5a-7824720cfeaf/raw","has_download":true,"has_spatial":true,"identifier":"/SDE/CMR_API/|C1374483929-NCCS","keyword":["earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-precipitation-atmosphere-precipitation-rate"],"last_harvested_date":"2026-10-07T00:36:17.484819","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":9,"publisher":"NASA/ARC/NAS/NEX;NASA/GSFC/SED/ESD/NCCS","slug":"nasa-earth-exchange-global-daily-downscaled-projections","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"NASA Earth Exchange Global Daily Downscaled Projections","type":"dataset"},{"_score":50.470913,"_sort":[1791333295374,50.470913,1,"6b771b4a-d210-4b2e-9532-abc6f2355579"],"access_level":"public","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":"The VCO IR2 CDR data set contains products acquired by the IR2 instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with an HDU as IMAGE extension, and it also contains metadata to the header of the HDU.","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-cdr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-cdr-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Planetary Atmospheres"},"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER IR2 CALIBRATED DATA V1.0"},"description":"The VCO IR2 CDR data set contains products acquired by the IR2 instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with an HDU as IMAGE extension, and it also contains metadata to the header of the HDU.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/8f91bdb6-060d-43e4-8881-c6115942c852","harvest_record_raw":"https://catalog.data.gov/harvest_record/8f91bdb6-060d-43e4-8881-c6115942c852/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-cdr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-cdr-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-10-07T00:34:55.374262","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":"venus-climate-orbiter-ir2-calibrated-data-v1-0-e12bf","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER IR2 CALIBRATED DATA V1.0","type":"dataset"},{"_score":49.372257,"_sort":[1791333189507,49.372257,1,"130b2639-a7f0-4e87-bbf0-fb0cc0a34adc"],"access_level":"public","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":"The VCO IR2 SEDR data set contains products of geometry information calculated by SPICE toolkit using SPICE kernels VCO-V-SPICE-6-V1.0, associated with images acquired by the IR2 instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with several HDUs as IMAGE extension, and it also contains metadata to the header of the HDUs.","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-sedr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-sedr-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Planetary Atmospheres"},"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER IR2 GEOMETRY INFORMATION V1.0"},"description":"The VCO IR2 SEDR data set contains products of geometry information calculated by SPICE toolkit using SPICE kernels VCO-V-SPICE-6-V1.0, associated with images acquired by the IR2 instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with several HDUs as IMAGE extension, and it also contains metadata to the header of the HDUs.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/74c5c0c8-d1dd-4c05-b9a6-72e2005a3dc7","harvest_record_raw":"https://catalog.data.gov/harvest_record/74c5c0c8-d1dd-4c05-b9a6-72e2005a3dc7/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-sedr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-ir2-3-sedr-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-10-07T00:33:09.507223","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":"venus-climate-orbiter-ir2-geometry-information-v1-0-8e9bf","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER IR2 GEOMETRY INFORMATION V1.0","type":"dataset"},{"_score":50.49398,"_sort":[1791333061895,50.49398,1,"7652fbdf-e5fb-4047-a47f-c7aaf178e1e8"],"access_level":"public","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":"The VCO UVI EDR data set contains products acquired by the UVI instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with an HDU as IMAGE extension, and it also contains metadata to the header of the HDU.","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-2-edr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-2-edr-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-28","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Planetary Atmospheres"},"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER UVI RAW DATA V1.0"},"description":"The VCO UVI EDR data set contains products acquired by the UVI instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with an HDU as IMAGE extension, and it also contains metadata to the header of the HDU.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/a25e8704-2f88-4d56-bf55-820b623e48e1","harvest_record_raw":"https://catalog.data.gov/harvest_record/a25e8704-2f88-4d56-bf55-820b623e48e1/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-2-edr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-2-edr-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-10-07T00:31:01.895540","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":"venus-climate-orbiter-uvi-raw-data-v1-0-f27e3","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER UVI RAW DATA V1.0","type":"dataset"},{"_score":12.736084,"_sort":[1791332895193,12.736084,1,"6fa675c6-a82e-4da2-9f2f-7c6faa2ca982"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_IIR_L1_CAL-Standard-V3-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Infrared ImagingRadiometer (IIR) Level 1 Calibration, Version 3-00 data product. The IIR calibration data product contains the Space View and Blackbody images with the gains derived in each channel. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006, to study the many roles clouds and aerosols play in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km(428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d.The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964848342-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_iir_l1_cal_v3-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964848342-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/IIR/CALIPSO/CAL_IIR_L1_CAL-Standard-V3-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964848342-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/IIR/CALIPSO/CAL_IIR_L1_CAL-Standard-V3-00","keyword":["earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO IIR Level 1 Calibration data"},"description":"CAL_IIR_L1_CAL-Standard-V3-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Infrared ImagingRadiometer (IIR) Level 1 Calibration, Version 3-00 data product. The IIR calibration data product contains the Space View and Blackbody images with the gains derived in each channel. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006, to study the many roles clouds and aerosols play in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km(428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d.The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1d2aa35e-156c-450c-b1f1-ec5a4247bea1","harvest_record_raw":"https://catalog.data.gov/harvest_record/1d2aa35e-156c-450c-b1f1-ec5a4247bea1/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/IIR/CALIPSO/CAL_IIR_L1_CAL-Standard-V3-00","keyword":["earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"last_harvested_date":"2026-10-07T00:28:15.193694","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":"calipso-iir-level-1-calibration-data","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO IIR Level 1 Calibration data","type":"dataset"},{"_score":10.8976345,"_sort":[1791332894007,10.8976345,1,"4fca6e92-0bf8-495a-af80-3737ea920992"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_IIR_L3_GEWEX_Cloud-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 3 Cloud products for the Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment. The IIR Level 3 GEWEX Cloud product reports global distributions of IIR cloud effective radius and water path averages and histograms on a uniform2-dimensional (2D) spatial grid. Cloud amount, radiative temperature, effective emissivity, and optical depth characterize the cloud samples for which IIR microphysical retrievals are reported. The statistics are reported for atmospheric columns containing only ice clouds, only liquid water clouds, and only high ice clouds of layer pressure lower than 440 hPa. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3908938884-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_iir_l3_gewexcloud_V2-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/LID_L3_GEWEX_Cloud-Standard-V2-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3908938884-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L3_GEWEX_Cloud-Standard-V2-00","keyword":["earth-science-clouds-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 20.2 km","temporal":"2006-06-01/2023-07-01","theme":["Earth Science"],"title":"CALIPSO IIR Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard V2-00"},"description":"CAL_IIR_L3_GEWEX_Cloud-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 3 Cloud products for the Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment. The IIR Level 3 GEWEX Cloud product reports global distributions of IIR cloud effective radius and water path averages and histograms on a uniform2-dimensional (2D) spatial grid. Cloud amount, radiative temperature, effective emissivity, and optical depth characterize the cloud samples for which IIR microphysical retrievals are reported. The statistics are reported for atmospheric columns containing only ice clouds, only liquid water clouds, and only high ice clouds of layer pressure lower than 440 hPa. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4452a593-6fec-4b49-b4b2-b4ceb9b9b3dd","harvest_record_raw":"https://catalog.data.gov/harvest_record/4452a593-6fec-4b49-b4b2-b4ceb9b9b3dd/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L3_GEWEX_Cloud-Standard-V2-00","keyword":["earth-science-clouds-atmosphere"],"last_harvested_date":"2026-10-07T00:28:14.007585","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":"calipso-iir-lidar-level-3-global-energy-and-water-cycle-experiment-gewex-cloud-standard-00-bd8f5","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO IIR Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard V2-00","type":"dataset"},{"_score":12.062894,"_sort":[1791332892143,12.062894,2,"2385e176-92c6-4a9d-b8ad-e648698f4262"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_IIR_L2_Swath-Standard-V5-00_V5-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Swath data. The IIR Level 2 swath data product contains half orbit of IIR emissivity and cloud particle data assigned to IIR pixels on a 1 km grid centered on the CALIPSO lidar (CALIOP) track. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964849224-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_iir_l2_v5-00_swath_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849224-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/IIR/CALIPSO/CAL_IIR_L2_Swath-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849224-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/IIR/CALIPSO/CAL_IIR_L2_Swath-Standard-V5-00","keyword":["earth-science-atmospheric-radiation-atmosphere-emissivity","earth-science-clouds-atmosphere-cloud-radiative-transfer","earth-science-infrared-wavelengths-spectral-engineering-brightness-temperature","earth-science-precipitation-atmosphere-precipitation-profiles"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Infrared Imaging Radiometer (IIR) Level 2 Swath, V5-00"},"description":"CAL_IIR_L2_Swath-Standard-V5-00_V5-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Swath data. The IIR Level 2 swath data product contains half orbit of IIR emissivity and cloud particle data assigned to IIR pixels on a 1 km grid centered on the CALIPSO lidar (CALIOP) track. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/54667fd4-fe53-4e98-a283-a2110f882481","harvest_record_raw":"https://catalog.data.gov/harvest_record/54667fd4-fe53-4e98-a283-a2110f882481/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/IIR/CALIPSO/CAL_IIR_L2_Swath-Standard-V5-00","keyword":["earth-science-atmospheric-radiation-atmosphere-emissivity","earth-science-clouds-atmosphere-cloud-radiative-transfer","earth-science-infrared-wavelengths-spectral-engineering-brightness-temperature","earth-science-precipitation-atmosphere-precipitation-profiles"],"last_harvested_date":"2026-10-07T00:28:12.143563","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/LARC/SD/ASDC","slug":"calipso-infrared-imaging-radiometer-iir-level-2-swath-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Infrared Imaging Radiometer (IIR) Level 2 Swath, V5-00","type":"dataset"},{"_score":12.081089,"_sort":[1791332891264,12.081089,1,"abd001d1-585a-41ea-9fbb-93e38b7aa7ca"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_IIR_L2_Track-Standard-V5-00_V5-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Track data. The IIR Level 2 track data product contains half orbit of IIR emissivity and cloud particle data related to pixels that have been co-located to the CALIPSO lidar (CALIOP) track. CALIPSO was a partnership between NASA and the French Space Agency, CNES. \nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964849248-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_iir_l2_v5-00_track_desc_qs_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849248-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/IIR/CALIPSO/CAL_IIR_L2_Track-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849248-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/IIR/CALIPSO/CAL_IIR_L2_Track-Standard-V5-00","keyword":["earth-science-atmospheric-radiation-atmosphere-emissivity","earth-science-clouds-atmosphere","earth-science-clouds-atmosphere-cloud-radiative-transfer","earth-science-infrared-wavelengths-spectral-engineering-brightness-temperature","earth-science-precipitation-atmosphere-precipitation-profiles"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2023-07-01","theme":["Earth Science"],"title":"CALIPSO Infrared Imaging Radiometer (IIR) Level 2 Track, V5-00"},"description":"CAL_IIR_L2_Track-Standard-V5-00_V5-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 2 Track data. The IIR Level 2 track data product contains half orbit of IIR emissivity and cloud particle data related to pixels that have been co-located to the CALIPSO lidar (CALIOP) track. CALIPSO was a partnership between NASA and the French Space Agency, CNES. \nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/891e0171-e222-44d6-ba1d-0fd7f2a0b50e","harvest_record_raw":"https://catalog.data.gov/harvest_record/891e0171-e222-44d6-ba1d-0fd7f2a0b50e/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/IIR/CALIPSO/CAL_IIR_L2_Track-Standard-V5-00","keyword":["earth-science-atmospheric-radiation-atmosphere-emissivity","earth-science-clouds-atmosphere","earth-science-clouds-atmosphere-cloud-radiative-transfer","earth-science-infrared-wavelengths-spectral-engineering-brightness-temperature","earth-science-precipitation-atmosphere-precipitation-profiles"],"last_harvested_date":"2026-10-07T00:28:11.264226","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":"calipso-infrared-imaging-radiometer-iir-level-2-track-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Infrared Imaging Radiometer (IIR) Level 2 Track, V5-00","type":"dataset"},{"_score":9.561422,"_sort":[1791332889762,9.561422,1,"0c4a279f-0f2f-4446-b736-5ccffcfbd79a"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_01kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 1 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud layer product, generated at a horizontal resolution of 1 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The cloud layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3463063995-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_lay_v5-00_desc_qs_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_01kmCLay-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3463063995-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_01kmCLay-Standard-V5-00","keyword":["earth-science-clouds-atmosphere-cloud-properties","earth-science-lidar-spectral-engineering-lidar-backscatter","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 20.2 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 1 km Cloud Layer, V5-00"},"description":"CAL_LID_L2_01kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 1 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud layer product, generated at a horizontal resolution of 1 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The cloud layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/39eb26e3-4add-493f-ac78-05fe0fa4bd75","harvest_record_raw":"https://catalog.data.gov/harvest_record/39eb26e3-4add-493f-ac78-05fe0fa4bd75/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_01kmCLay-Standard-V5-00","keyword":["earth-science-clouds-atmosphere-cloud-properties","earth-science-lidar-spectral-engineering-lidar-backscatter","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"last_harvested_date":"2026-10-07T00:28:09.762392","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":"calipso-lidar-level-2-1-km-cloud-layer-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 1 km Cloud Layer, V5-00","type":"dataset"},{"_score":9.531668,"_sort":[1791332888940,9.531668,2,"9c3a5b19-d220-458d-b30c-9ff3d5d0fb58"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_333mMLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 333 m Merged Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.\n\nWithin this cloud and aerosol layer product, generated at a horizontal resolution of 333 m (single shot resolution), are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The merged layer products consist of a sequence of column descriptors, each associated with a variable number of cloud and aerosol layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud and aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. \n\nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3551627915-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_lay_v5-00_desc_qs_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_333mMLay-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3551627915-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_333mMLay-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 8.25 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 1/3 km Merged Layer, V5-00"},"description":"CAL_LID_L2_333mMLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 333 m Merged Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.\n\nWithin this cloud and aerosol layer product, generated at a horizontal resolution of 333 m (single shot resolution), are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The merged layer products consist of a sequence of column descriptors, each associated with a variable number of cloud and aerosol layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud and aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. \n\nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5d846e2e-dd64-41ba-b7ed-e8c968d5dc48","harvest_record_raw":"https://catalog.data.gov/harvest_record/5d846e2e-dd64-41ba-b7ed-e8c968d5dc48/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_333mMLay-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types"],"last_harvested_date":"2026-10-07T00:28:08.940615","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/LARC/SD/ASDC","slug":"calipso-lidar-level-2-1-3-km-merged-layer-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 1/3 km Merged Layer, V5-00","type":"dataset"},{"_score":9.609962,"_sort":[1791332887954,9.609962,2,"efe96cff-44a0-492f-9261-ce7e7a421709"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_05kmALay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this aerosol layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The aerosol layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.","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/C3463063987-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_lay_v5-00_desc_qs_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3463063987-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-aerosols-atmosphere-dust-ash-smoke"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 30.1 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 5 km Aerosol Layer Data, V5-00"},"description":"CAL_LID_L2_05kmALay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this aerosol layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The aerosol layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d63a8408-24ba-41c8-ac42-d0f27573540a","harvest_record_raw":"https://catalog.data.gov/harvest_record/d63a8408-24ba-41c8-ac42-d0f27573540a/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-aerosols-atmosphere-dust-ash-smoke"],"last_harvested_date":"2026-10-07T00:28:07.954700","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/LARC/SD/ASDC","slug":"calipso-lidar-level-2-5-km-aerosol-layer-data-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 5 km Aerosol Layer Data, V5-00","type":"dataset"},{"_score":9.599962,"_sort":[1791332887143,9.599962,1,"be23f76a-c569-46c5-b1c1-56836aec4567"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_05kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The cloud layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3463063986-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_lay_v5-00_desc_qs_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3463063986-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Standard-V5-00","keyword":["earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 30.1 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 5km Cloud Layer, V5-00"},"description":"CAL_LID_L2_05kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The cloud layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/fdaaa16b-dc37-4006-8de3-2dd504f26d82","harvest_record_raw":"https://catalog.data.gov/harvest_record/fdaaa16b-dc37-4006-8de3-2dd504f26d82/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Standard-V5-00","keyword":["earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types"],"last_harvested_date":"2026-10-07T00:28:07.143432","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":"calipso-lidar-level-2-5km-cloud-layer-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 5km Cloud Layer, V5-00","type":"dataset"},{"_score":9.936775,"_sort":[1791332885704,9.936775,5,"81d8cbda-04cd-4f0d-880b-4679bb06075e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_05kmAPro-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. \n\nThis aerosol profile product reports vertical profiles of particulate extinction and backscatter, as well as additional information (e.g. particulate depolarization ratios) derived from these fundamental measurements. The aerosol profile products are reported at a uniform spatial resolution of 60 m vertically and 5 km horizontally, over a nominal altitude range from 30 km to -0.5 km. Due to constraints imposed by the on-board data averaging scheme, the vertical resolution of the aerosol profile data varies as a function of altitude. In the tropospheric region between 20 km to -0.5 km, the aerosol profile products are reported at a resolution of 60 m vertically, and in the stratospheric region(above 20-km), the aerosol profile products are reported at a resolution of 180 m vertically.\n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. \n\nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3551627908-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_pro_v5-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmAPro-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3551627908-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmAPro-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-clouds-atmosphere-cloud-types","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 30 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Aerosol Profile, V5-00"},"description":"CAL_LID_L2_05kmAPro-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. \n\nThis aerosol profile product reports vertical profiles of particulate extinction and backscatter, as well as additional information (e.g. particulate depolarization ratios) derived from these fundamental measurements. The aerosol profile products are reported at a uniform spatial resolution of 60 m vertically and 5 km horizontally, over a nominal altitude range from 30 km to -0.5 km. Due to constraints imposed by the on-board data averaging scheme, the vertical resolution of the aerosol profile data varies as a function of altitude. In the tropospheric region between 20 km to -0.5 km, the aerosol profile products are reported at a resolution of 60 m vertically, and in the stratospheric region(above 20-km), the aerosol profile products are reported at a resolution of 180 m vertically.\n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. \n\nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f915ee83-14cc-4b9f-aacc-2fea01c7d608","harvest_record_raw":"https://catalog.data.gov/harvest_record/f915ee83-14cc-4b9f-aacc-2fea01c7d608/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmAPro-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-clouds-atmosphere-cloud-types","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"last_harvested_date":"2026-10-07T00:28:05.704000","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":"NASA/LARC/SD/ASDC","slug":"calipso-lidar-level-2-aerosol-profile-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Aerosol Profile, V5-00","type":"dataset"},{"_score":11.761154,"_sort":[1791332884483,11.761154,1,"e3a265e2-ce71-4753-8391-8296190fb3c6"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_MLay-Diagnostic-Beta-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2Merged Layer Diagnostic data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The contents of this merged layer product contain information already in the CAL_LID_L2_05kmMLay-Standard-V5-00 andCAL_LID_L2_333mMLay-Standard-V5-00 data products, but with additional diagnostic flags and parameters that were used by the science algorithms. \nCALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964849109-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_lay_v5-00_desc_qs_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849109-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_MLay_Diagnostic-Beta-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849109-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_MLay_Diagnostic-Beta-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Maximum Altitude, Minimum Altitude, 30.1 km, -0.5 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Merged Layer Diagnostic Beta, V5-00"},"description":"CAL_LID_L2_MLay-Diagnostic-Beta-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2Merged Layer Diagnostic data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The contents of this merged layer product contain information already in the CAL_LID_L2_05kmMLay-Standard-V5-00 andCAL_LID_L2_333mMLay-Standard-V5-00 data products, but with additional diagnostic flags and parameters that were used by the science algorithms. \nCALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ad1a20a1-119d-483c-b92d-9acfd159f92e","harvest_record_raw":"https://catalog.data.gov/harvest_record/ad1a20a1-119d-483c-b92d-9acfd159f92e/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_MLay_Diagnostic-Beta-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"last_harvested_date":"2026-10-07T00:28:04.483965","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":"calipso-lidar-level-2-merged-layer-diagnostic-beta-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Merged Layer Diagnostic Beta, V5-00","type":"dataset"},{"_score":10.954357,"_sort":[1791332883048,10.954357,1,"d8727706-2848-47fa-b327-43a9ac99a9e0"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_PSCMask-Standard-V3-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Polar Stratospheric Clouds (PSC) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and describes the spatial distribution, optical properties, and composition of PSC layers observed. The product contains profiles of PSC presence, composition, optical properties, and meteorological information on a uniform 5-km horizontal x 180-m vertical grid along CALIPSO orbit tracks. Aura Microwave Limb Sounder (MLS) measurements of the primary PSC condensable vapors HNO3 and H2O and a number of parameters from the Aura MLS V2 Derived Meteorological Products (DMPs) are also included in this product. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. \n\nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3551627684-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_pscmask_v3-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_PSCMask-Standard-V3-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3551627684-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_PSCMask-Standard-V3-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-extinction","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-atmospheric-radiation-atmosphere-scattering","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-types","earth-science-clouds-atmosphere-stratospheric-clouds-observed-analyzed","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, 8.3 km, 30.1 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Polar Stratospheric Cloud (PSC), V3-00"},"description":"CAL_LID_L2_PSCMask-Standard-V3-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Polar Stratospheric Clouds (PSC) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and describes the spatial distribution, optical properties, and composition of PSC layers observed. The product contains profiles of PSC presence, composition, optical properties, and meteorological information on a uniform 5-km horizontal x 180-m vertical grid along CALIPSO orbit tracks. Aura Microwave Limb Sounder (MLS) measurements of the primary PSC condensable vapors HNO3 and H2O and a number of parameters from the Aura MLS V2 Derived Meteorological Products (DMPs) are also included in this product. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. \n\nCALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f86c20bc-d407-44c8-9421-d94ec0e2788c","harvest_record_raw":"https://catalog.data.gov/harvest_record/f86c20bc-d407-44c8-9421-d94ec0e2788c/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_PSCMask-Standard-V3-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-backscatter","earth-science-aerosols-atmosphere-aerosol-extinction","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness","earth-science-atmospheric-radiation-atmosphere-scattering","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles","earth-science-clouds-atmosphere-cloud-types","earth-science-clouds-atmosphere-stratospheric-clouds-observed-analyzed","earth-science-lidar-spectral-engineering-lidar-depolarization-ratio"],"last_harvested_date":"2026-10-07T00:28:03.048788","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":"calipso-lidar-level-2-polar-stratospheric-cloud-psc-v3-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Polar Stratospheric Cloud (PSC), V3-00","type":"dataset"},{"_score":12.017243,"_sort":[1791332881984,12.017243,3,"8a99b66f-7feb-4f36-a19f-2c8465a17f20"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L2_VFM-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP Level 2 VFM data product contains scene classification and lidar lighting and land/water indicators. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3463064020-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l2_vfm_v5-00_desc_qs_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_VFM-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3463064020-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_VFM-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere-dust-ash-smoke","earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 30.1 km","temporal":"2006-06-12/2023-06-30","theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Vertical Feature Mask (VFM), V5-00"},"description":"CAL_LID_L2_VFM-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP Level 2 VFM data product contains scene classification and lidar lighting and land/water indicators. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/37d470a2-9287-44a7-8e70-3f6c724bf637","harvest_record_raw":"https://catalog.data.gov/harvest_record/37d470a2-9287-44a7-8e70-3f6c724bf637/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/CAL_LID_L2_VFM-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere-dust-ash-smoke","earth-science-clouds-atmosphere-cloud-microphysics","earth-science-clouds-atmosphere-cloud-properties","earth-science-clouds-atmosphere-cloud-types"],"last_harvested_date":"2026-10-07T00:28:01.984240","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":"calipso-lidar-level-2-vertical-feature-mask-vfm-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 2 Vertical Feature Mask (VFM), V5-00","type":"dataset"},{"_score":12.09413,"_sort":[1791332880629,12.09413,3,"22b4ffdd-3c4d-4eaf-9012-f5366f07b5d5"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L3_Cloud_Occurrence-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar(CALIOP) Level 3 Cloud Occurrence products. The Lidar Level 3 Cloud Occurrence product reports global distributions of cloud occurrence by counts on a uniform spatial grid. At each grid, the number of detected ice cloud samples is also reported as a histogram of ice cloud layer optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964849739-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l3_cloud-occur_v2-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849739-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cnes.fr/projets/calipso","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/L3_CLOUD_OCCURRENCE-STANDARD-V2-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849739-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/L3_CLOUD_OCCURRENCE-STANDARD-V2-00","keyword":["earth-science-clouds-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.5 km, 20.2 km","temporal":"2006-06-01/2023-07-01","theme":["Earth Science"],"title":"CALIPSO Lidar Level 3 Cloud Occurrence Data, Standard V2-00"},"description":"CAL_LID_L3_Cloud_Occurrence-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar(CALIOP) Level 3 Cloud Occurrence products. The Lidar Level 3 Cloud Occurrence product reports global distributions of cloud occurrence by counts on a uniform spatial grid. At each grid, the number of detected ice cloud samples is also reported as a histogram of ice cloud layer optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/894296c7-74e5-4b2d-90a1-83d820e4bca5","harvest_record_raw":"https://catalog.data.gov/harvest_record/894296c7-74e5-4b2d-90a1-83d820e4bca5/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/L3_CLOUD_OCCURRENCE-STANDARD-V2-00","keyword":["earth-science-clouds-atmosphere"],"last_harvested_date":"2026-10-07T00:28:00.629208","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":"calipso-lidar-level-3-cloud-occurrence-data-standard-v2-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 3 Cloud Occurrence Data, Standard V2-00","type":"dataset"},{"_score":10.019865,"_sort":[1791332878416,10.019865,5,"80f24dc3-c971-4283-a0ad-aac28d9a48c0"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, All-Sky, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product, generated separately between day and night, reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is a tropospheric product, so data are only reported below altitudes of 12 km. All parameters are derived from the version 5.00 CALIOP Level 2 data and have been quality screened prior to averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distributional information are also included. The All-Sky designate indicates that all level 2 columns are averaged, regardless of the occurrence of clouds. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964849274-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l3_tropoaero_V5-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849274-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/LID_L3_Tropospheric_APro_AllSky-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849274-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/LID_L3_Tropospheric_APro_AllSky-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-extinction","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.4 km, 12.1 km","temporal":"2006-06-01/2023-07-01","theme":["Earth Science"],"title":"CALIPSO Lidar Level 3 Tropospheric Aerosol Profiles, All Sky Data, Standard V5-00"},"description":"CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, All-Sky, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product, generated separately between day and night, reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is a tropospheric product, so data are only reported below altitudes of 12 km. All parameters are derived from the version 5.00 CALIOP Level 2 data and have been quality screened prior to averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distributional information are also included. The All-Sky designate indicates that all level 2 columns are averaged, regardless of the occurrence of clouds. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/300116fc-b14f-4ecf-93bf-308ebd4622fa","harvest_record_raw":"https://catalog.data.gov/harvest_record/300116fc-b14f-4ecf-93bf-308ebd4622fa/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/LID_L3_Tropospheric_APro_AllSky-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-extinction","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness"],"last_harvested_date":"2026-10-07T00:27:58.416647","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":"NASA/LARC/SD/ASDC","slug":"calipso-lidar-level-3-tropospheric-aerosol-profiles-all-sky-data-standard-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 3 Tropospheric Aerosol Profiles, All Sky Data, Standard V5-00","type":"dataset"},{"_score":9.968864,"_sort":[1791332877420,9.968864,0,"31c451a8-1811-40f6-a343-ec32a2ed5904"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation(CALIPSO) Lidar Level 3 Tropospheric Aerosol, Cloud-Free, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product, generated separately between day and night, reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is a tropospheric product, so data are only reported below altitudes of 12 km. All parameters are derived from the version 5.00 CALIOP Level 2 data and have been quality screened prior to averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distributional information are also included. The Cloud-Free designate indicates that only cloud-free level 2 columns are averaged. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964849262-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_lid_l3_tropoaero_V5-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849262-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/CALIOP/CALIPSO/LID_L3_Tropospheric_APro_CloudFree-Standard-V5-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849262-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/CALIOP/CALIPSO/LID_L3_Tropospheric_APro_CloudFree-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-extinction","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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}]}}]], Minimum Altitude, Maximum Altitude, -0.4 km, 12.1 km","temporal":"2006-06-01/2023-07-01","theme":["Earth Science"],"title":"CALIPSO Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard V5-00"},"description":"CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation(CALIPSO) Lidar Level 3 Tropospheric Aerosol, Cloud-Free, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product, generated separately between day and night, reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is a tropospheric product, so data are only reported below altitudes of 12 km. All parameters are derived from the version 5.00 CALIOP Level 2 data and have been quality screened prior to averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distributional information are also included. The Cloud-Free designate indicates that only cloud-free level 2 columns are averaged. \n\nCALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d4c5ec86-348a-42bb-992a-479d432c88cd","harvest_record_raw":"https://catalog.data.gov/harvest_record/d4c5ec86-348a-42bb-992a-479d432c88cd/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/CALIOP/CALIPSO/LID_L3_Tropospheric_APro_CloudFree-Standard-V5-00","keyword":["earth-science-aerosols-atmosphere","earth-science-aerosols-atmosphere-aerosol-extinction","earth-science-aerosols-atmosphere-aerosol-optical-depth-thickness"],"last_harvested_date":"2026-10-07T00:27:57.420342","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/LARC/SD/ASDC","slug":"calipso-lidar-level-3-tropospheric-aerosol-profiles-cloud-free-data-standard-v5-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard V5-00","type":"dataset"},{"_score":11.490168,"_sort":[1791332874085,11.490168,1,"461b163c-f7ac-4d45-bf08-92fd752ceda1"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_WFC_L1_1Km-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B 1 km Version 4-00 data product. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The 1 km Native Science grid covers the full 61 km swath centered on the Lidar track. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964848876-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_wfc_l1_1Km_v4-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964848876-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/WFC/CALIPSO/CAL_WFC_L1_1Km-Standard-V4-00_L1B-004.00","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964848876-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_1Km-Standard-V4-00_L1B-004.00","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2020-04-10","theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B 1 km Native Science data, Standard V4-00"},"description":"CAL_WFC_L1_1Km-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B 1 km Version 4-00 data product. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The 1 km Native Science grid covers the full 61 km swath centered on the Lidar track. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5c337bf2-7a39-492a-b84b-99971765b4bb","harvest_record_raw":"https://catalog.data.gov/harvest_record/5c337bf2-7a39-492a-b84b-99971765b4bb/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_1Km-Standard-V4-00_L1B-004.00","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"last_harvested_date":"2026-10-07T00:27:54.085428","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":"calipso-wide-field-camera-level-1b-1-km-native-science-data-standard-v4-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B 1 km Native Science data, Standard V4-00","type":"dataset"},{"_score":11.430086,"_sort":[1791332872899,11.430086,1,"ce4c129a-d851-4a69-afd0-49d53c80cd2c"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_WFC_L1_125m-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B 125 m Version 4-00 data product. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The 125 m Native Science grid contains only the central 5km wide high-resolution portion of the WFC swath. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.","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/C3964848826-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_wfc_l1_125m_v4-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964848826-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/WFC/CALIPSO/CAL_WFC_L1_125m-Standard-V4-00_L1B-004.00","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964848826-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_125m-Standard-V4-00_L1B-004.00","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2020-04-10","theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B 125m Native Science data, Standard V4-00"},"description":"CAL_WFC_L1_125m-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B 125 m Version 4-00 data product. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The 125 m Native Science grid contains only the central 5km wide high-resolution portion of the WFC swath. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/dab83d82-304f-4dcd-aeb6-b4c957981537","harvest_record_raw":"https://catalog.data.gov/harvest_record/dab83d82-304f-4dcd-aeb6-b4c957981537/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_125m-Standard-V4-00_L1B-004.00","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"last_harvested_date":"2026-10-07T00:27:52.899745","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":"calipso-wide-field-camera-level-1b-125m-native-science-data-standard-v4-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B 125m Native Science data, Standard V4-00","type":"dataset"},{"_score":11.761154,"_sort":[1791332871720,11.761154,1,"f585e5c2-44bc-4f8f-9714-94a11f10a0b9"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_WFC_L1_Asm-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Assembler Version 4-00 data product. The WFC Level 1B Assembler data products contain geolocated nighttime daily digital count and daily statistical data which comes from the calibration product. The Assembler takes the calibration data and calculates the statistics foreach pixel (mean, standard deviation, maximum, and minimum). CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964849036-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_wfc_l1_asm_v4-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849036-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/WFC/CALIPSO/CAL_WFC_L1_Asm-Standard-V4-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849036-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_Asm-Standard-V4-00","keyword":["earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2020-04-10","theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B Assembler data, V4-00"},"description":"CAL_WFC_L1_Asm-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Assembler Version 4-00 data product. The WFC Level 1B Assembler data products contain geolocated nighttime daily digital count and daily statistical data which comes from the calibration product. The Assembler takes the calibration data and calculates the statistics foreach pixel (mean, standard deviation, maximum, and minimum). CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/843d4b85-d23b-4a9c-8fcb-59ccade01ba9","harvest_record_raw":"https://catalog.data.gov/harvest_record/843d4b85-d23b-4a9c-8fcb-59ccade01ba9/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_Asm-Standard-V4-00","keyword":["earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"last_harvested_date":"2026-10-07T00:27:51.720825","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":"calipso-wide-field-camera-level-1b-assembler-data-v4-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B Assembler data, V4-00","type":"dataset"},{"_score":12.081089,"_sort":[1791332870525,12.081089,1,"70e14377-668c-4a0e-89d0-5dfdcb55feec"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_WFC_L1_Cal-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Calibration Version 4-00 data product. The WFC Level 1B Calibration data products contain geolocated nighttime digital count data. The calibration data is collected over a 25-second segment on the dark side of every orbit. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.","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/C3964849107-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_wfc_l1_cal_v4-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964849107-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/WFC/CALIPSO/CAL_WFC_L1_Cal-Standard-V4-00","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964849107-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_Cal-Standard-V4-00","keyword":["earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2020-04-10","theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B Calibration data, V4-00"},"description":"CAL_WFC_L1_Cal-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Calibration Version 4-00 data product. The WFC Level 1B Calibration data products contain geolocated nighttime digital count data. The calibration data is collected over a 25-second segment on the dark side of every orbit. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a6f8f9b0-0ef1-48bd-820b-595f58d0cb4c","harvest_record_raw":"https://catalog.data.gov/harvest_record/a6f8f9b0-0ef1-48bd-820b-595f58d0cb4c/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_Cal-Standard-V4-00","keyword":["earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"last_harvested_date":"2026-10-07T00:27:50.525064","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":"calipso-wide-field-camera-level-1b-calibration-data-v4-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B Calibration data, V4-00","type":"dataset"},{"_score":10.940184,"_sort":[1791332869533,10.940184,1,"f72e9727-23ed-4801-ac44-66fa6abc5420"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"CAL_WFC_L1_IIR-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B IIR Version 4-00 data product. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B IIR Registered Science grid provides WFC data on the identical grid as the CALIPSO IIR data and is produced to facilitate the use of the WFC data in the IIR retrievals. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","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/C3964848890-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/calipso/CALIPSO_DPC_Rev5x00.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/quality_summaries/cal_wfc_l1_iir_v4-00_desc_qs.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Browse_Image_Tutorial.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_FAQ_mod2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Payload.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/documents/calipso/user_guides/CALIPSO_Data_Users_Guide_Peer_Reviewed_Bibliography.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/CALIPSO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/static/images/project_logos/calipso.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://calipso.cnes.fr/fr","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3964848890-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/WFC/CALIPSO/CAL_WFC_L1_IIR-Standard-V4-00_L1B-004.00","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ntrs.nasa.gov/citations/20260000771","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/calipso/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3964848890-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_IIR-Standard-V4-00_L1B-004.00","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","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":"2006-06-12/2020-04-10","theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B Science data, Standard V4-00"},"description":"CAL_WFC_L1_IIR-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B IIR Version 4-00 data product. The primary WFC Level 1B data products are calibrated radiance and bidirectional reflectance registered to an Earth-based grid centered on the Lidar ground track. During normal operation, the WFC acquires science data only during the daylight portions of the CALIPSO orbits. The Wide Field Camera Level 1B IIR Registered Science grid provides WFC data on the identical grid as the CALIPSO IIR data and is produced to facilitate the use of the WFC data in the IIR retrievals. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth\u2019s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the \u201cC-Train\u201d. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/661409bc-5331-40b7-9166-104c57545f01","harvest_record_raw":"https://catalog.data.gov/harvest_record/661409bc-5331-40b7-9166-104c57545f01/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/WFC/CALIPSO/CAL_WFC_L1_IIR-Standard-V4-00_L1B-004.00","keyword":["earth-science-aerosols-atmosphere-aerosol-radiance","earth-science-atmospheric-radiation-atmosphere-reflectance","earth-science-infrared-wavelengths-spectral-engineering-infrared-radiance","earth-science-visible-wavelengths-spectral-engineering-visible-radiance"],"last_harvested_date":"2026-10-07T00:27:49.533814","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":"calipso-wide-field-camera-level-1b-science-data-standard-v4-00","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CALIPSO Wide Field Camera Level 1B Science data, Standard V4-00","type":"dataset"},{"_score":30.813816,"_sort":[1791332836739,30.813816,1,"5a7ef89f-7809-4caa-abdb-d7c0a7ce1f60"],"access_level":"public","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-2-edr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.mro-m-mcs-2-edr-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Planetary Atmospheres"},"theme":["Planetary Science"],"title":"MRO MARS CLIMATE SOUNDER LEVEL 2 EDR V1.0"},"description":"Unknown","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/9e12b975-3d68-4421-8f2c-418325dcfd7b","harvest_record_raw":"https://catalog.data.gov/harvest_record/9e12b975-3d68-4421-8f2c-418325dcfd7b/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.mro-m-mcs-2-edr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.mro-m-mcs-2-edr-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-10-07T00:27:16.739318","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-2-edr-v1-0-36d23","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"MRO MARS CLIMATE SOUNDER LEVEL 2 EDR V1.0","type":"dataset"},{"_score":14.971905,"_sort":[1791332619781,14.971905,1,"eb9b1028-fa15-40b6-b289-58f2c97ef235"],"access_level":"public","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 20-year tree cover (TC) estimates at 30-m spatial resolution for Mexico from 2000 to 2019 using Landsat time series, airborne LiDAR, and machine learning. The TC data (hereafter, CMS-TC) offers accurate and consistent national-scale percent tree cover estimates with an overall coefficient of determination (R squared) of 0.81 and a root mean square error (RMSE) of 11.90%. The CMS-TC product is essential for tracking tree cover and aboveground biomass changes, monitoring land use dynamics, supporting biodiversity conservation, and informing climate and land use policy decisions. 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/C3707308646-ORNL_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://daac.ornl.gov/CMS/guides/Tree_Canopy_Cover_Mexico_V2_Fig1.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://data.ornldaac.earthdata.nasa.gov/public/cms/Tree_Canopy_Cover_Mexico_V2/comp/Tree_Canopy_Cover_Mexico_V2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.3334/ORNLDAAC/2445","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3707308646-ORNL_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.3334/ORNLDAAC/2445","keyword":["earth-science-ecosystems-biosphere-terrestrial-ecosystems","earth-science-topography-land-surface-terrain-elevation","earth-science-vegetation-biosphere-vegetation-cover"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"ORNL_DAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -119.226, \"NorthBoundingCoordinate\": 33.7967, \"EastBoundingCoordinate\": -84.6252, \"SouthBoundingCoordinate\": 13.4057}]]","temporal":"2000-01-01/2019-12-31","theme":["Earth Science"],"title":"CMS: Tree Cover Estimates at 30 m Resolution for Mexico from 2000 to 2019"},"description":"This dataset provides 20-year tree cover (TC) estimates at 30-m spatial resolution for Mexico from 2000 to 2019 using Landsat time series, airborne LiDAR, and machine learning. The TC data (hereafter, CMS-TC) offers accurate and consistent national-scale percent tree cover estimates with an overall coefficient of determination (R squared) of 0.81 and a root mean square error (RMSE) of 11.90%. The CMS-TC product is essential for tracking tree cover and aboveground biomass changes, monitoring land use dynamics, supporting biodiversity conservation, and informing climate and land use policy decisions. The data are provided in GeoTIFF format.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d9c88bcf-1214-403f-b4e4-f04b3d757744","harvest_record_raw":"https://catalog.data.gov/harvest_record/d9c88bcf-1214-403f-b4e4-f04b3d757744/raw","has_download":true,"has_spatial":true,"identifier":"10.3334/ORNLDAAC/2445","keyword":["earth-science-ecosystems-biosphere-terrestrial-ecosystems","earth-science-topography-land-surface-terrain-elevation","earth-science-vegetation-biosphere-vegetation-cover"],"last_harvested_date":"2026-10-07T00:23:39.781867","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":"cms-tree-cover-estimates-at-30-m-resolution-for-mexico-from-2000-to-2019","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"CMS: Tree Cover Estimates at 30 m Resolution for Mexico from 2000 to 2019","type":"dataset"},{"_score":51.259563,"_sort":[1791332582580,51.259563,2,"903fd722-5160-4bdc-8c37-45ded8ff96e5"],"access_level":"public","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":"The VCO UVI CDR data set contains products acquired by the UVI instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with an HDU as IMAGE extension, and it also contains metadata to the header of the HDU.","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-3-cdr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-3-cdr-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-29","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Planetary Atmospheres"},"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER UVI CALIBRATED DATA V1.0"},"description":"The VCO UVI CDR data set contains products acquired by the UVI instrument onboard the Venus Climate Orbiter (VCO, also known as PLANET-C and AKATSUKI) spacecraft. The data files are provided in FITS format with an HDU as IMAGE extension, and it also contains metadata to the header of the HDU.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/d5263f70-0ae5-4208-a874-02b99e73822e","harvest_record_raw":"https://catalog.data.gov/harvest_record/d5263f70-0ae5-4208-a874-02b99e73822e/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-3-cdr-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.vco-v-uvi-3-cdr-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-10-07T00:23:02.580683","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":"Planetary Atmospheres","slug":"venus-climate-orbiter-uvi-calibrated-data-v1-0-487c2","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"VENUS CLIMATE ORBITER UVI CALIBRATED DATA V1.0","type":"dataset"},{"_score":30.813816,"_sort":[1791332530478,30.813816,1,"451d38a9-2695-4448-9567-faf5f4d04cff"],"access_level":"public","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":"2026-09-29","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/4629886a-3c9a-4ca4-b78b-54531ed0e04b","harvest_record_raw":"https://catalog.data.gov/harvest_record/4629886a-3c9a-4ca4-b78b-54531ed0e04b/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":"2026-10-07T00:22:10.478836","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":27.394682,"_sort":[1791332453968,27.394682,1,"2d4edf1f-9338-4994-95c9-9c1aad995ab6"],"access_level":"public","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":"2026-09-29","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/99954e41-4550-4c5a-bba7-2529042ebc8f","harvest_record_raw":"https://catalog.data.gov/harvest_record/99954e41-4550-4c5a-bba7-2529042ebc8f/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":"2026-10-07T00:20:53.968320","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-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":10.508007,"_sort":[1791332453623,10.508007,1,"0e63b564-a902-478d-836e-d2b5c12e92ce"],"access_level":"public","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":"2026-09-29","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/73344001-9a08-4f75-b269-369b32342989","harvest_record_raw":"https://catalog.data.gov/harvest_record/73344001-9a08-4f75-b269-369b32342989/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":"2026-10-07T00:20:53.623710","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-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":19.691328,"_sort":[1791332451567,19.691328,1,"5ddaf510-e103-4514-8b0a-da930656ad1f"],"access_level":"public","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":"2026-09-29","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/99eb71cf-4a48-42f3-b437-7191a44e02ac","harvest_record_raw":"https://catalog.data.gov/harvest_record/99eb71cf-4a48-42f3-b437-7191a44e02ac/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/nj0x-8y67","keyword":["earth-science-land-use-land-cover-land-surface"],"last_harvested_date":"2026-10-07T00:20:51.567788","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":10.888827,"_sort":[1791332450115,10.888827,4,"d25fd9d6-8bc5-427c-90d2-963b6107ed88"],"access_level":"public","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":"2026-09-29","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/9e79e6e0-d476-458d-b419-8f1e35707d0a","harvest_record_raw":"https://catalog.data.gov/harvest_record/9e79e6e0-d476-458d-b419-8f1e35707d0a/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":"2026-10-07T00:20:50.115431","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-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":10.508007,"_sort":[1791332449755,10.508007,4,"660c8dad-ef3f-421b-b723-864540370450"],"access_level":"public","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":"2026-09-29","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/d656e612-fdde-4a54-b3e8-ccccd5bdef02","harvest_record_raw":"https://catalog.data.gov/harvest_record/d656e612-fdde-4a54-b3e8-ccccd5bdef02/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":"2026-10-07T00:20:49.755409","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-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":10.611626,"_sort":[1791332448426,10.611626,2,"f73a9a54-1e44-405c-b3b5-f2143f27bc22"],"access_level":"public","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/C3540911288-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3540911288-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":"2026-09-29","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/f06f1a96-b247-4dbe-b429-3d73753d1407","harvest_record_raw":"https://catalog.data.gov/harvest_record/f06f1a96-b247-4dbe-b429-3d73753d1407/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":"2026-10-07T00:20:48.426048","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":"millennium-ecosystem-assessment-ma-ecosystems-1a004","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"Millennium Ecosystem Assessment: MA Ecosystems","type":"dataset"},{"_score":44.096443,"_sort":[1791332448056,44.096443,2,"9e4ecf9a-2bca-43b9-9175-01d9096f3b50"],"access_level":"public","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/C3550186856-ESDIS.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3550186856-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/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":"2026-09-29","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/ef204e2c-25d4-4f88-b972-a209f4ef710c","harvest_record_raw":"https://catalog.data.gov/harvest_record/ef204e2c-25d4-4f88-b972-a209f4ef710c/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":"2026-10-07T00:20:48.056872","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":"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":18.892307,"_sort":[1791332447003,18.892307,1,"70d2bdac-b366-4e66-82da-12e67f1181b1"],"access_level":"public","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":"2026-09-29","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/a18bd504-0097-49c8-90df-8ce199660594","harvest_record_raw":"https://catalog.data.gov/harvest_record/a18bd504-0097-49c8-90df-8ce199660594/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H41C1TT4","keyword":["earth-science-air-quality-atmosphere-emissions"],"last_harvested_date":"2026-10-07T00:20:47.003185","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":"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":16.65681,"_sort":[1791332446670,16.65681,1,"83caa4e8-e75b-49d2-a1a3-498595f2a151"],"access_level":"public","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":"2026-09-29","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/88bbdddd-b565-4b0c-9c1a-9febc8c6d258","harvest_record_raw":"https://catalog.data.gov/harvest_record/88bbdddd-b565-4b0c-9c1a-9febc8c6d258/raw","has_download":true,"has_spatial":true,"identifier":"10.7927/H4HD7SKJ","keyword":["earth-science-air-quality-atmosphere-emissions"],"last_harvested_date":"2026-10-07T00:20:46.670447","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":"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":59.292557,"_sort":[1791332446304,59.292557,2,"4b238897-5878-4e89-ae71-b4671af13a14"],"access_level":"public","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":"2026-09-29","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/47c8ca95-3d47-46d5-a3de-da82d9899220","harvest_record_raw":"https://catalog.data.gov/harvest_record/47c8ca95-3d47-46d5-a3de-da82d9899220/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":"2026-10-07T00:20:46.304748","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-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":13.852645,"_sort":[1791332438032,13.852645,1,"ed7d5b66-611e-4b9f-ba2d-6b244430d812"],"access_level":"public","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":"2026-09-29","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":"2014-01-31/2026-09-21","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/338aedb7-3edb-4bef-a4bd-20d64b43b5d5","harvest_record_raw":"https://catalog.data.gov/harvest_record/338aedb7-3edb-4bef-a4bd-20d64b43b5d5/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":"2026-10-07T00:20:38.032824","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":41.842243,"_sort":[1791332437701,41.842243,0,"a7eda918-9c52-497b-89b5-3486c7a8aaed"],"access_level":"public","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":"2026-09-29","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/a5f52806-6190-48a9-9f5f-c6f5684bbd0f","harvest_record_raw":"https://catalog.data.gov/harvest_record/a5f52806-6190-48a9-9f5f-c6f5684bbd0f/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":"2026-10-07T00:20:37.701962","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":41.253536,"_sort":[1791332432960,41.253536,0,"13630234-a50c-4464-bae1-d3b3dcb19417"],"access_level":"public","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":"2026-09-29","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/c8f985b3-1750-4ee1-8d4b-80e7b565697e","harvest_record_raw":"https://catalog.data.gov/harvest_record/c8f985b3-1750-4ee1-8d4b-80e7b565697e/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":"2026-10-07T00:20:32.960869","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":43.296406,"_sort":[1791332431915,43.296406,1,"802e1906-5fb4-4152-8023-1efe4011518a"],"access_level":"public","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":"2026-09-29","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":"2005-11-20/2026-09-21","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/e452f95a-c2c7-4d18-9ffa-290ca6feab35","harvest_record_raw":"https://catalog.data.gov/harvest_record/e452f95a-c2c7-4d18-9ffa-290ca6feab35/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":"2026-10-07T00:20:31.915741","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":14.219898,"_sort":[1791332431564,14.219898,1,"e20e7778-6099-4f4f-af3f-c06f47dc615b"],"access_level":"public","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/C4054954664-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/L1C_ATBD_GPM_V08.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/V08_L1C_Release_Notes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954664-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954664-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_1CNOAA20ATMS_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_1CNOAA20ATMS.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=C4054954664-GES_DISC&q=GPM_1CNOAA20ATMS_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/snppatms.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":"2026-09-29","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":"2014-01-31/2026-09-21","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/ab63e1c0-a72c-4f09-b737-f4788a01bf8d","harvest_record_raw":"https://catalog.data.gov/harvest_record/ab63e1c0-a72c-4f09-b737-f4788a01bf8d/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":"2026-10-07T00:20:31.564478","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":43.296406,"_sort":[1791332430891,43.296406,1,"3a9efab4-8b43-44ca-8934-d68f866ab368"],"access_level":"public","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":"2026-09-29","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":"2006-12-14/2026-09-21","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/08f97b6f-5cf8-4ee7-9a93-88758008203b","harvest_record_raw":"https://catalog.data.gov/harvest_record/08f97b6f-5cf8-4ee7-9a93-88758008203b/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":"2026-10-07T00:20:30.891452","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-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":14.309409,"_sort":[1791332430558,14.309409,1,"4cc149f8-7872-479a-8857-cc19042ae383"],"access_level":"public","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":"2026-09-29","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":"2014-01-31/2026-09-21","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/62ccd31d-d495-40f2-a737-857e353fd94d","harvest_record_raw":"https://catalog.data.gov/harvest_record/62ccd31d-d495-40f2-a737-857e353fd94d/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":"2026-10-07T00:20:30.558726","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":43.768105,"_sort":[1791332430175,43.768105,1,"8d88dc1b-6f5f-4521-9c42-a44e03a632a1"],"access_level":"public","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":"2026-09-29","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":"2009-12-01/2026-09-21","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/1188ce5c-dc49-49b4-b918-a709ae8ffa68","harvest_record_raw":"https://catalog.data.gov/harvest_record/1188ce5c-dc49-49b4-b918-a709ae8ffa68/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":"2026-10-07T00:20:30.175347","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":14.309409,"_sort":[1791332429833,14.309409,0,"49f87fe2-0f88-4e35-84c1-9d4cf922664d"],"access_level":"public","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":"2026-09-29","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":"2014-01-31/2026-09-21","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/5e54085c-0554-47d0-91fd-9124cbc6b854","harvest_record_raw":"https://catalog.data.gov/harvest_record/5e54085c-0554-47d0-91fd-9124cbc6b854/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":"2026-10-07T00:20:29.833308","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":43.128036,"_sort":[1791332429160,43.128036,1,"199092f9-50c2-4e2c-a6c4-56bd79c4f00b"],"access_level":"public","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":"2026-09-29","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/d031cae4-e31a-420f-899f-dda52e0f7039","harvest_record_raw":"https://catalog.data.gov/harvest_record/d031cae4-e31a-420f-899f-dda52e0f7039/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":"2026-10-07T00:20:29.160917","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":11.388905,"_sort":[1791332410944,11.388905,2,"41c2dc56-37bd-40e7-a95d-a4104667f072"],"access_level":"public","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":"2026-09-29","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":"2012-01-02/2026-09-21","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/1b957295-51d2-4b8f-b707-df668a7d6e5f","harvest_record_raw":"https://catalog.data.gov/harvest_record/1b957295-51d2-4b8f-b707-df668a7d6e5f/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":"2026-10-07T00:20:10.944891","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"}],"sort":"last_harvested_date"}