Timeline / Data.gov — Climate Datasets
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A new raw object was archived. Both versions are preserved. 3714 line(s) added, 4594 line(s) removed.
Evidence
| Source | Data.gov — Climate Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=climate&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-10-07T00:16:58+00:00 |
| Content type | application/json |
| Current object |
669de073e2688d1ead9a5ee32e5c0e4385168c72a7f41d24e653925955b5b79b
download raw
metadata
|
| Previous object |
2d912b934e589f22b441689c21bbded4d12ff30b61782dfbfb0a7e6ffc7de6f9
download raw
metadata
|
What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-10-07T00:16:58+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 3714 line(s) added, 4594 line(s) removed.
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This filename defines the following:\nSpecies name = Acakoa (Acacia koa), \nIsland = Hawaii island, \nFile type = 80 pct, indicating that it is a binary raster of habitat suitability where a value of 1 means 80% of model iterations forecast suitable habitat, and a value of 0 means less than 80% of model runs project suitability, \nClimate trajectory = future, which represents the point in the future (2090) where the lower, middle and upper trajectories converge, \nYear = 2090 (end of century since that's when our climate data set series ends).", + "fn": "Earthdata Forum", + "hasEmail": "mailto:earthdata-support@nasa.gov" + }, + "description": "The Climate Fingerprinting Sounder Product (ClimFiSP) Version 2 provides Level 3 daily and monthly atmospheric data including temperature profiles, water vapor, ozone, cloud properties, as well as surface properties, including skin temperature and emissivity. Data are available on a ½ by ½ degree grid. 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ClimFISP retrievals derived from each instrument are provided separately to facilitate detection of potential radiance biases between the CHIRP-AIRS and CHIRP-CrIS data records, and to evaluate their impact on the long-term data record formed by merging AIRS and CrIS data.", "distribution": [ { "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P1LJW4J4", - "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.57b27182e4b00148d3982d6b.xml", - "format": "XML", + "conformsTo": "http://www.isotc211.org/2005/gmi", + "description": "The metadata's original source.", + "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3892833504-GES_DISC.iso19115", + "format": "ISO", "mediaType": "text/xml", "title": "Original Metadata" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://disc.gsfc.nasa.gov/datacollection/SNDRSNIL3SMCFSP_2.html", + "format": "HTML", + "mediaType": "text/html" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/SNDRSNIL3SMCFSP_2.png", + "format": "PNG", + "mediaType": "image/png" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://docserver.gesdisc.eosdis.nasa.gov/public/project/Sounder/ClimFiSP.V2.ATBD.pdf", + "format": "PDF", + "mediaType": "application/pdf" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://docserver.gesdisc.eosdis.nasa.gov/public/project/Sounder/ClimFiSP.V2.README_03182026.pdf", + "format": "PDF", + "mediaType": "application/pdf" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3892833504-GES_DISC", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://sounder.gesdisc.eosdis.nasa.gov/data/SNPP_Sounder_Level3/SNDRSNIL3SMCFSP.2/", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://sounder.gesdisc.eosdis.nasa.gov/opendap/SNPP_Sounder_Level3/SNDRSNIL3SMCFSP.2/", + "format": "BIN", + "mediaType": "application/octet-stream" } ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57b27182e4b00148d3982d6b", - "keyword": [ - "Hawaii Volcanoes National Park", - "Hawaiian Islands", - "USGS:57b27182e4b00148d3982d6b", - "climate change", - "climatologyMeteorologyAtmosphere", - "envelope model", - "environment", - "geospatial datasets", - "modeling", - "species distribution", - "species range" - ], - "modified": "2026-10-02T00:00:00Z", + "identifier": "10.5067/OO4X2XO5WG38", + "keyword": [ + "earth-science-air-quality-atmosphere-tropospheric-ozone", + "earth-science-altitude-atmosphere-tropopause", + "earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds", + "earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds", + "earth-science-atmospheric-pressure-atmosphere-surface-pressure", + "earth-science-atmospheric-radiation-atmosphere-outgoing-longwave-radiation", + "earth-science-atmospheric-temperature-atmosphere-surface-temperature", + "earth-science-atmospheric-temperature-atmosphere-upper-air-temperature", + "earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators", + "earth-science-atmospheric-water-vapor-atmosphere-water-vapor-profiles", + "earth-science-clouds-atmosphere-cloud-properties", + "earth-science-ocean-temperature-oceans-sea-surface-temperature", + "earth-science-precipitation-atmosphere-precipitation-rate", + "earth-science-surface-radiative-properties-land-surface-emissivity", + "earth-science-surface-thermal-properties-land-surface-skin-temperature" + ], + "license": "https://www.usa.gov/government-works", + "modified": "2026-09-29", + "programCode": [ + "026:000" + ], "publisher": { "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-159.80025, 18.88725, -154.80525, 22.25325", - "theme": [ - "geospatial" - ], - "title": "Plant species range models under different climate scenarios in Hawaii 2000-2090" - }, - "description": "This is the primary output dataset from the project to access the potential impacts of climate change on vegetation management strategies within Hawaii Volcanoes National Park (HAVO). The key objective of this project was to combine climate projections from the International Pacific Research Center (IPRC) and plant distribution models from Price et al. to produce a series of projected species range maps over the next century. Although the project focused on HAVO, the projected species range maps were created for seven of the main Hawaiian Islands. We stored the model output as rasters (.TIF files); additionally we created multi-panel maps of these rasters that are available separately.\nIn summary, this dataset consists of 4,095 rasters that delineate plant species range, both present and future, for various climate change scenarios and years. The series covers 39 species, 7 islands, and 15 different combinations of climate trajectory and year. The contents of each raster varies slightly, but the contents can be determined from the specific filename. Filenames have a consistent naming convenion, as follows:\nSpecies name + island + file type + climate trajectory + year.TIF, where the following definitions apply:\nSpecies name = abbreviated code representing genus and species;\nIsland = 1 of the main 7 Hawaiian Islands (Hawaii, Maui, Kahoolwe, Lanai, Molokai, Oahu, and Kauai);\nFile type = one of 3 file types: \n(1) RANGE = present species range as of year 2000,\n(2) 80 PCT = binary raster of habitat suitability,\n(3) CHANGE TO 80 = raster showing the change in suitability between the year 2000 and the year indicated in the file name; \nClimate trajectory = lower (concave upward trajectory of change in rainfall and temperature over the century), middle (linear change in rainfall and temperature), upper (concave downward trajectory of change in rainfall and temperature), or future (where all three trajectories converge in 2090); \nYear = one of the following years: 2000, 2040, 2070, or 2090.\nFor example, consider this filename: Acakoa Hawaii 80 pct future2090.tif. 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Long-term Projections Report OCE-2011-1and location based on proximity to ethanol plants and topography. Urban projection map served as basemap, which used NLCD 2001 V2 as the basemap. Values:11 Open Water12 Ice/Snow21 Developed, Open Space22 Developed, Low Intensity23 Developed, Medium Intensity24 Developed, High Intensity31 Barren Land41 Deciduous Forest42 Evergreen Forest43 Mixed Forest52 Scrub/Shrub71 Grasslands81 Pasture/Hay82 Cultivated Crops90 Woody Wetlands90 Emergent Herbaceous Wetlands99 New Developed Land since 2001, no class specified1XX transitioned to agriculture from original category XX", + "fn": "Earthdata Forum", + "hasEmail": "mailto:earthdata-support@nasa.gov" + }, + "description": "The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VJ243D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ243D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ243D product contains just one data variable. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ243MA1](https://doi.org/10.5067/VIIRS/VJ243MA1.002) product is stored in a separate file as VJ243D01 through VJ243D36. In addition to the bands included in VJ243MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ243D37 through VJ243D39. Details regarding methodology are available on the VJ243MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\n\nVJ243D03 is the BRDF geometric parameter for VIIRS band M1 (0.412 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1.\n\nKnown Issues\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2&as=5200).", "distribution": [ { "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P142M5FZ", - "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.50903111e4b0a1b43c29caab.xml", - "format": "XML", + "conformsTo": "http://www.isotc211.org/2005/gmi", + "description": "The metadata's original source.", + "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C2842750282-LPCLOUD.iso19115", + "format": "ISO", "mediaType": "text/xml", "title": "Original Metadata" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://doi.org/10.5067/VIIRS/VJ243D03.002", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ladsweb.modaps.eosdis.nasa.gov/filespec/VIIRS/1/VNP43D03", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://landweb.modaps.eosdis.nasa.gov", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf", + "format": "PDF", + "mediaType": "application/pdf" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C2842750282-LPCLOUD", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://viirsland.gsfc.nasa.gov/Val/Albedo_Val.html", + "format": "HTML", + "mediaType": "text/html" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://viirsland.gsfc.nasa.gov/Val_overview.html", + "format": "HTML", + "mediaType": "text/html" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.earthdata.nasa.gov/centers/lp-daac", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43d-cmg-30-arc-second-products/", + "format": "BIN", + "mediaType": "application/octet-stream" } ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_50903111e4b0a1b43c29caab", - "keyword": [ - "Agriculture", - "Farmland", - "Projections", - "USGS:50903111e4b0a1b43c29caab", - "climate change", - "environment", - "future", - "geospatial", - "geospatial datasets", - "land cover", - "land use", - "modeling" - ], - "modified": "2026-10-02T00:00:00Z", + "identifier": "10.5067/VIIRS/VJ243D03.002", + "keyword": [ + "earth-science-surface-radiative-properties-land-surface-albedo", + "earth-science-surface-radiative-properties-land-surface-anisotropy", + "earth-science-surface-radiative-properties-land-surface-reflectance" + ], + "license": "https://www.usa.gov/government-works", + "modified": "2026-09-29", + "programCode": [ + "026:000" + ], "publisher": { "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-127.8871, 22.9408, -65.3460, 51.6048", - "theme": [ - "geospatial" - ], - "title": "United States 2040 Land Cover Projection (Agriculture Expansion) 300m" - }, - "description": "Agriculture growth projection amount based on USDA (2011) Agricultural Projections to 2020. Long-term Projections Report OCE-2011-1and location based on proximity to ethanol plants and topography. Urban projection map served as basemap, which used NLCD 2001 V2 as the basemap. Values:11 Open Water12 Ice/Snow21 Developed, Open Space22 Developed, Low Intensity23 Developed, Medium Intensity24 Developed, High Intensity31 Barren Land41 Deciduous Forest42 Evergreen Forest43 Mixed Forest52 Scrub/Shrub71 Grasslands81 Pasture/Hay82 Cultivated Crops90 Woody Wetlands90 Emergent Herbaceous Wetlands99 New Developed Land since 2001, no class specified1XX transitioned to agriculture from original category XX", + "name": "LP DAAC;NASA/GSFC/SED/ESD/TISL/LandSIPS;UMASS-B/SFE" + }, + "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]", + "temporal": "2023-02-10/2026-09-21", + "theme": [ + "Earth Science" + ], + "title": "VIIRS/JPSS2 BRDF/Albedo Parameter 3 Band M1 Daily L3 Global 30 ArcSec CMG V002" + }, + "description": "The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VJ243D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ243D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ243D product contains just one data variable. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ243MA1](https://doi.org/10.5067/VIIRS/VJ243MA1.002) product is stored in a separate file as VJ243D01 through VJ243D36. In addition to the bands included in VJ243MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ243D37 through VJ243D39. Details regarding methodology are available on the VJ243MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\n\nVJ243D03 is the BRDF geometric parameter for VIIRS band M1 (0.412 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1.\n\nKnown Issues\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2&as=5200).", "distribution_titles": [ - "Digital Data", "Original Metadata" ], - "harvest_record": "https://catalog.data.gov/harvest_record/3bbbb2a6-eee7-4c2f-aed9-bc82c2af266e", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/3bbbb2a6-eee7-4c2f-aed9-bc82c2af266e/raw", + "harvest_record": "https://catalog.data.gov/harvest_record/a06fc10f-aa5f-41f8-8dfd-70eae25c66ab", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/a06fc10f-aa5f-41f8-8dfd-70eae25c66ab/raw", "has_download": true, "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_50903111e4b0a1b43c29caab", - "keyword": [ - "Agriculture", - "Farmland", - "Projections", - "USGS:50903111e4b0a1b43c29caab", - "climate change", - "environment", - "future", - "geospatial", - "geospatial datasets", - "land cover", - "land use", - "modeling" - ], - "last_harvested_date": "2026-10-05T04:06:15.459510", + "identifier": "10.5067/VIIRS/VJ243D03.002", + "keyword": [ + "earth-science-surface-radiative-properties-land-surface-albedo", + "earth-science-surface-radiative-properties-land-surface-anisotropy", + "earth-science-surface-radiative-properties-land-surface-reflectance" + ], + "last_harvested_date": "2026-10-07T00:16:27.562422", "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", + "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": "doi" + "slug": "nasa" }, "parent_identifier": null, - "popularity": 0, - "publisher": "U.S. Geological Survey", - "slug": "united-states-2040-land-cover-projection-agriculture-expansion-300m", - "spatial_centroid": { - "lat": 34.4064, - "lon": -102.87066 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -127.8871, - 22.9408 - ], - [ - -127.8871, - 51.6048 - ], - [ - -65.346, - 51.6048 - ], - [ - -65.346, - 22.9408 - ], - [ - -127.8871, - 22.9408 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "United States 2040 Land Cover Projection (Agriculture Expansion) 300m", + "popularity": 1, + "publisher": "LP DAAC;NASA/GSFC/SED/ESD/TISL/LandSIPS;UMASS-B/SFE", + "slug": "viirs-jpss2-brdf-albedo-parameter-3-band-m1-daily-l3-global-30-arcsec-cmg-v002", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [ + "Earth Science" + ], + "title": "VIIRS/JPSS2 BRDF/Albedo Parameter 3 Band M1 Daily L3 Global 30 ArcSec CMG V002", "type": "dataset" }, { - "_score": 10.0427685, + "_score": 14.8022175, "_sort": [ - 1791172979718, - 10.0427685, - 0, - "7d5db995-a3a7-4820-beca-79b43a00adc3" + 1791332186833, + 14.8022175, + 1, + "65b015e0-6c26-49ad-9d8d-5827dfa48560" ], "access_level": "public", "dcat": { + "@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": [ - "010:12" + "026:00" ], "contactPoint": { "@type": "vcard:Contact", - "fn": "Joseph J. 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Both monthly values and long-term monthly averages are made available, including the climatological measure for standard deviation and coefficient of variation. \nDewes et al. (2017) used this dataset to evaluate the ability of different E0 formulations – Hargreaves-Samani, Priestly-Taylor, and Penman-Monteith – to reproduce the spatial patterns of observed warm-season E0 and its interannual variability. This data is an extension of the dataset described in Hobbins (2004) and Hobbins et al. (2004) with 21 additional stations north of 41oN latitude. The extension was needed in order to include data in the North Central Climate Science Center region. For these added stations, the procedure described in Hobbins (2004) for quality control was applied, including an adjustment in the mean when documented station moves occurred, and the removal of obvious outliers. The quality control procedure for the extended dataset did not automate tests for undocumented inhomogeneities for these stations. For all stations, a visual inspection of the timeseries was used to add additional breakpoints in the data for homogenization (only two were added in the extended set), and to eliminate two stations from consideration.", + "fn": "Earthdata Forum", + "hasEmail": "mailto:earthdata-support@nasa.gov" + }, + "description": "The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VJ221C2) combines the daily ([VJ221A1D](http://doi.org/10.5067/VIIRS/VJ221A1D.002)) and ([VJ221A1N](http://doi.org/10.5067/VIIRS/VJ221A1N.002)) products over an 8-day compositing period into a single product. The VJ221C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VJ221A1D and VJ221A1N daily acquisitions from the 8-day period. Unlike the VJ221A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ221C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) variables within a single Hierarchical Data Format (HDF) file. \n\nThe overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VJ121C2 product contains 27 variables: LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121C2 granule.\n\nKnown Issues:\n\n•\tFor complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2&as=5200).", "distribution": [ { "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.21429/C9MW25", - "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.59124c44e4b0e541a03c20de.xml", - "format": "XML", + "conformsTo": "http://www.isotc211.org/2005/gmi", + "description": "The metadata's original source.", + "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C2832144235-LPCLOUD.iso19115", + "format": "ISO", "mediaType": "text/xml", "title": "Original Metadata" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VJ221C2.002/VJ221C2.A2025361.002.2026013013434/BROWSE.VJ221C2.A2025361.002.2026013013434.1.jpg", + "format": "JPEG", + "mediaType": "image/jpeg" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://doi.org/10.5067/VIIRS/VJ221C2.002", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://landweb.modaps.eosdis.nasa.gov", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf", + "format": "PDF", + "mediaType": "application/pdf" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://lpdaac.usgs.gov/documents/1662/VNP21_User_Guide_V2.pdf", + "format": "PDF", + "mediaType": "application/pdf" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C2832144235-LPCLOUD", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://viirsland.gsfc.nasa.gov/Val/LST_Val.html", + "format": "HTML", + "mediaType": "text/html" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://viirsland.gsfc.nasa.gov/Val_overview.html", + "format": "HTML", + "mediaType": "text/html" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.earthdata.nasa.gov/centers/lp-daac", + "format": "BIN", + "mediaType": "application/octet-stream" } ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59124c44e4b0e541a03c20de", - "keyword": [ - "USGS:59124c44e4b0e541a03c20de", - "droughts", - "elevation", - "modeling" - ], - "modified": "2026-10-02T00:00:00Z", + "identifier": "10.5067/VIIRS/VJ221C2.002", + "keyword": [ + "earth-science-surface-radiative-properties-land-surface-emissivity", + "earth-science-surface-thermal-properties-land-surface-land-surface-temperature" + ], + "license": "https://www.usa.gov/government-works", + "modified": "2026-09-29", + "programCode": [ + "026:000" + ], "publisher": { "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-180.0, -90.0, 180.0, 90.0", - "theme": [ - "geospatial" - ], - "title": "Monthly Pan Evaporation Data across the Continental United States between 1950-2001" - }, - "description": "Pan evaporation is a measure of atmospheric evaporative demand (E0) for which long term and spatially distributed observations are available from the NOAA Cooperative Observer (COOP) Network. 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For all stations, a visual inspection of the timeseries was used to add additional breakpoints in the data for homogenization (only two were added in the extended set), and to eliminate two stations from consideration.", + "name": "LP DAAC;NASA/GSFC/SED/ESD/TISL/LandSIPS" + }, + "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]", + "temporal": "2023-02-10/2026-09-21", + "theme": [ + "Earth Science" + ], + "title": "VIIRS/JPSS2 Land Surface Temperature/Emissivity 8-Day L3 Global 0.05Deg CMG V002" + }, + "description": "The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VJ221C2) combines the daily ([VJ221A1D](http://doi.org/10.5067/VIIRS/VJ221A1D.002)) and ([VJ221A1N](http://doi.org/10.5067/VIIRS/VJ221A1N.002)) products over an 8-day compositing period into a single product. The VJ221C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VJ221A1D and VJ221A1N daily acquisitions from the 8-day period. Unlike the VJ221A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ221C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) variables within a single Hierarchical Data Format (HDF) file. \n\nThe overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).\n\nThe VJ121C2 product contains 27 variables: LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121C2 granule.\n\nKnown Issues:\n\n•\tFor complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2&as=5200).", "distribution_titles": [ - "Digital Data", "Original Metadata" ], - "harvest_record": "https://catalog.data.gov/harvest_record/3f22d75a-1607-4378-b7a9-646159114218", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/3f22d75a-1607-4378-b7a9-646159114218/raw", + "harvest_record": "https://catalog.data.gov/harvest_record/8e491149-b9d2-4da5-938b-782c0eaf54a8", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/8e491149-b9d2-4da5-938b-782c0eaf54a8/raw", "has_download": true, "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59124c44e4b0e541a03c20de", - "keyword": [ - "USGS:59124c44e4b0e541a03c20de", - "droughts", - "elevation", - "modeling" - ], - "last_harvested_date": "2026-10-05T04:02:59.718571", + "identifier": "10.5067/VIIRS/VJ221C2.002", + "keyword": [ + "earth-science-surface-radiative-properties-land-surface-emissivity", + "earth-science-surface-thermal-properties-land-surface-land-surface-temperature" + ], + "last_harvested_date": "2026-10-07T00:16:26.833845", "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", + "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": "doi" + "slug": "nasa" }, "parent_identifier": null, - "popularity": 0, - "publisher": "U.S. Geological Survey", - "slug": "monthly-pan-evaporation-data-across-the-continental-united-states-between-1950-2001", - "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": "Monthly Pan Evaporation Data across the Continental United States between 1950-2001", + "popularity": 1, + "publisher": "LP DAAC;NASA/GSFC/SED/ESD/TISL/LandSIPS", + "slug": "viirs-jpss2-land-surface-temperature-emissivity-8-day-l3-global-0-05deg-cmg-v002", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [ + "Earth Science" + ], + "title": "VIIRS/JPSS2 Land Surface Temperature/Emissivity 8-Day L3 Global 0.05Deg CMG V002", "type": "dataset" }, { - "_score": 10.583361, + "_score": 10.431437, "_sort": [ - 1791172920047, - 10.583361, - 2, - "27e328f6-730c-488b-a075-d5c51314dd06" + 1791332186459, + 10.431437, + 0, + "256b53fb-bb05-41ab-9528-227a68d399af" ], "access_level": "public", "dcat": { + "@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": [ - "010:12" + "026:00" ], "contactPoint": { "@type": "vcard:Contact", - "fn": "Dean Gesch", - "hasEmail": "mailto:gesch@usgs.gov" - }, - "description": "As a low-lying island nation, the Republic of the Marshall Islands (RMI) is at the forefront of exposure to climate change impacts, including, primarily, inundation (coastal flooding). 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When properly characterized, the vertical accuracy of the high-resolution, high-accuracy elevation data can be used to generate maps and report assessment results with the uncertainty stated in terms of a specific confidence level, which is the approach employed here. This data release includes the results of a quantitative assessment of inundation exposure for Ebon Island in Ebon Atoll, including rigorous accounting for the vertical uncertainty in the input elevation model data. 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When properly characterized, the vertical accuracy of the high-resolution, high-accuracy elevation data can be used to generate maps and report assessment results with the uncertainty stated in terms of a specific confidence level, which is the approach employed here. This data release includes the results of a quantitative assessment of inundation exposure for Ebon Island in Ebon Atoll, including rigorous accounting for the vertical uncertainty in the input elevation model data. 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The 81 sets can be viewed as a 3x3x3x3 matrix, created based on a combination of three GCMs from the CMIP5 archive (CCSM4, MIROC5, and MPI-ESM-LR), each of which simulated 21st century climate responses for three different future atmospheric composition scenarios (known as representative concentration pathways or RCPs 2.6, 4.5, and 8.5). Three different SD techniques were employed, and each used three gridded observation-based data products to train (i.e. calibrate) the SD methods. The three downscaling techniques include a delta method (DeltaSD), an equi-distant quantile mapping method (EDQM), and a piecewise asynchronous regression method (PARM). The observational data products used for training were Daymet v. 2.1, Livneh v. 1.2, and PRISM AN81d v. D1. The resulting SD-processed projections are on a 10 km by 10 km grid covering the south-central United States (all of AR, KS, LA, NM, OK, TX, and portions of CO and MO). Both historical baseline files (1981-2005) and future projections (2006-2099) are provided, as appropriate.\nThough not exhaustive, these downscaled climate projections for the south central US region represent a range of potential future climate trajectories that can serve as a component of climate impacts research studies. That 81 sets of future projections, and not just one, are provided is indicative that some uncertainties exist regarding the trajectory of the 21st century climate change, though all show notable warming. Uncertainties in how human activity may change future atmospheric composition are represented by the different RCP scenarios. Differences in how sensitive the surface climate of this region will be to atmospheric composition changes are sampled by the use of different GCMs. Similarly, because each SD method has different performance characteristics and observational products differ, the use of different SD techniques and training data set combinations acknowledges that SD methodological choices influence the value-added statistically refined climate projection data products. Applied researchers may explore aspects of their applications’ sensitivities to some climate projection uncertainties by sampling from these 81 sets of SD data products. 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The 81 sets can be viewed as a 3x3x3x3 matrix, created based on a combination of three GCMs from the CMIP5 archive (CCSM4, MIROC5, and MPI-ESM-LR), each of which simulated 21st century climate responses for three different future atmospheric composition scenarios (known as representative concentration pathways or RCPs 2.6, 4.5, and 8.5). Three different SD techniques were employed, and each used three gridded observation-based data products to train (i.e. calibrate) the SD methods. The three downscaling techniques include a delta method (DeltaSD), an equi-distant quantile mapping method (EDQM), and a piecewise asynchronous regression method (PARM). The observational data products used for training were Daymet v. 2.1, Livneh v. 1.2, and PRISM AN81d v. D1. The resulting SD-processed projections are on a 10 km by 10 km grid covering the south-central United States (all of AR, KS, LA, NM, OK, TX, and portions of CO and MO). 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Differences in how sensitive the surface climate of this region will be to atmospheric composition changes are sampled by the use of different GCMs. Similarly, because each SD method has different performance characteristics and observational products differ, the use of different SD techniques and training data set combinations acknowledges that SD methodological choices influence the value-added statistically refined climate projection data products. Applied researchers may explore aspects of their applications’ sensitivities to some climate projection uncertainties by sampling from these 81 sets of SD data products. 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They include results from a system designed to alert managers when a local population begins to behave differently from the larger regional population. Although these changes are detected using counts of male birds at breeding areas ('leks'), the final information is reported at a broader geographic scale called a neighborhood cluster, as lek locations are considered ecologically sensitive.\nSage-grouse are at the center of state and national land-use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with the best-available science to help guide management and conservation plans aimed at benefitting sage-grouse populations and the ecosystems they inhabit. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022) and incorporates population lek count data for 1960-2025. Included in this report are methodological updates to lek count data aggregation, state-space model forecasting, and targed annual warning system (TAWS) signals, which are detailed under individual Modification sections. State-space models estimated 2.7-percent average annual decline in sage-grouse populations between 1966 and 2021 (Period 1-7, six population oscillations) across their geographical range. Average annual decline among climate clusters for the same number of oscillations ranged between 2.0 and 3.5 percent. Cumulative declines were 42.4, 61.7, and 77.9 percent range-wide during Period 5-7 (19 years), Period 3-7 (35 years), and Period 1-7 (55 years), respectively. Between 2024 and 2025 (one-year, non-trend timeframe), range-wide population size declined 1.9 percent (95-percent CRI = -4.71-1.48).\nDefinitions:\nWatch: A TAWS alert level assigned to a population unit (lek or NC) when slow signals are detected in 2 consecutive years. A \nWatch^r represents the first formal, multi-year evidence of a sustained decoupled decline at the local scale and calls for heightened monitoring attention.\nAcute Warning: A TAWS alert level assigned to a population unit (lek or NC) when slow signals are detected in 3 of 4 consecutive years, or fast signals in 2 of 3 consecutive years. A Warning^r represents sustained, statistically aberrant decline relative to the regional trend, a pattern inconsistent with climate-driven variation alone. A Warning^r concurrently activates a chronic Warning^N. The superscript (r) designates that this alert is based on intrinsic rate of change in abundance (r-hat) divergence from the regional trend, distinguishing it from the abundance-referenced chronic Warning^N designation.\nChronic Warning^N: A TAWS alert category that activates concurrently alongside a Warning^r. Unlike the Warning^r which is a binary, annual assessment based on intrinsic rate of change in abundance (r-hat) divergence from the regional trend, the chronic Warning^N remains in effect beyond the year of Warning^r activation, providing an ongoing record of whether the population has demonstrated a meaningful rebound in abundance. The chronic Warning^N is removed only once the population unit’s estimated abundance rises to match or exceed a projected recovery threshold (Target Abundance). This threshold is derived from the climate cluster’s rate of change in abundance as applied to the population unit abundance, prior to the initial signal that led to the Warning^N. This design ensures that a Warning is not prematurely lifted from population units that have ceased to decline (and decouple) but have not yet recovered to a biologically meaningful level of abundance that accounts for broader trends in population change. \nWatches may identify the need for intensive monitoring whereas warnings may identify the need for management intervention aimed at stabilizing populations.\nPlease refer to the work in the Larger Works citation for a complete glossary of terms.\nReferences:\nCoates, P.S., Prochazka, B.G., O’Donnell, M.S., Aldridge, C.L., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2021, Range-wide greater sage-grouse hierarchical monitoring framework-Implications for defining population boundaries, trend estimation, and a targeted annual warning system: U.S. Geological Survey Open-File Report 2020-1154, 243 p., https://doi.org/10.3133/ofr20201154.\nCoates, P.S., Prochazka, B.G., Aldridge, C.L., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)-Updated 1960-2021: U.S. Geological Survey Data Report 1165, 16 p., https://doi.org/10.3133/dr1165", + "fn": "Earthdata Forum", + "hasEmail": "mailto:earthdata-support@nasa.gov" + }, + "description": "This dataset provides Global Navigation Satellite System (GNSS) Radio Occultation (RO) Excess Phase and Amplitude data from the GRACE (Gravity Recovery and Climate Experiment) mission as contributed by the University Corporation for Atmospheric Research. 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They include results from a system designed to alert managers when a local population begins to behave differently from the larger regional population. Although these changes are detected using counts of male birds at breeding areas ('leks'), the final information is reported at a broader geographic scale called a neighborhood cluster, as lek locations are considered ecologically sensitive.\nSage-grouse are at the center of state and national land-use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with the best-available science to help guide management and conservation plans aimed at benefitting sage-grouse populations and the ecosystems they inhabit. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022) and incorporates population lek count data for 1960-2025. Included in this report are methodological updates to lek count data aggregation, state-space model forecasting, and targed annual warning system (TAWS) signals, which are detailed under individual Modification sections. State-space models estimated 2.7-percent average annual decline in sage-grouse populations between 1966 and 2021 (Period 1-7, six population oscillations) across their geographical range. Average annual decline among climate clusters for the same number of oscillations ranged between 2.0 and 3.5 percent. Cumulative declines were 42.4, 61.7, and 77.9 percent range-wide during Period 5-7 (19 years), Period 3-7 (35 years), and Period 1-7 (55 years), respectively. Between 2024 and 2025 (one-year, non-trend timeframe), range-wide population size declined 1.9 percent (95-percent CRI = -4.71-1.48).\nDefinitions:\nWatch: A TAWS alert level assigned to a population unit (lek or NC) when slow signals are detected in 2 consecutive years. A \nWatch^r represents the first formal, multi-year evidence of a sustained decoupled decline at the local scale and calls for heightened monitoring attention.\nAcute Warning: A TAWS alert level assigned to a population unit (lek or NC) when slow signals are detected in 3 of 4 consecutive years, or fast signals in 2 of 3 consecutive years. A Warning^r represents sustained, statistically aberrant decline relative to the regional trend, a pattern inconsistent with climate-driven variation alone. A Warning^r concurrently activates a chronic Warning^N. The superscript (r) designates that this alert is based on intrinsic rate of change in abundance (r-hat) divergence from the regional trend, distinguishing it from the abundance-referenced chronic Warning^N designation.\nChronic Warning^N: A TAWS alert category that activates concurrently alongside a Warning^r. Unlike the Warning^r which is a binary, annual assessment based on intrinsic rate of change in abundance (r-hat) divergence from the regional trend, the chronic Warning^N remains in effect beyond the year of Warning^r activation, providing an ongoing record of whether the population has demonstrated a meaningful rebound in abundance. The chronic Warning^N is removed only once the population unit’s estimated abundance rises to match or exceed a projected recovery threshold (Target Abundance). This threshold is derived from the climate cluster’s rate of change in abundance as applied to the population unit abundance, prior to the initial signal that led to the Warning^N. This design ensures that a Warning is not prematurely lifted from population units that have ceased to decline (and decouple) but have not yet recovered to a biologically meaningful level of abundance that accounts for broader trends in population change. \nWatches may identify the need for intensive monitoring whereas warnings may identify the need for management intervention aimed at stabilizing populations.\nPlease refer to the work in the Larger Works citation for a complete glossary of terms.\nReferences:\nCoates, P.S., Prochazka, B.G., O’Donnell, M.S., Aldridge, C.L., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2021, Range-wide greater sage-grouse hierarchical monitoring framework-Implications for defining population boundaries, trend estimation, and a targeted annual warning system: U.S. Geological Survey Open-File Report 2020-1154, 243 p., https://doi.org/10.3133/ofr20201154.\nCoates, P.S., Prochazka, B.G., Aldridge, C.L., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)-Updated 1960-2021: U.S. Geological Survey Data Report 1165, 16 p., https://doi.org/10.3133/dr1165", + "name": "NASA/GSFC/SED/ESD/TISL/GESDISC" + }, + "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]", + "temporal": "2007-02-28/2026-09-21", + "theme": [ + "Earth Science" + ], + "title": "GNSS radio occultation L1B excess phase and amplitude for GRACE as contributed by UCAR, V2.0 (gnssro_grace_ucar_l1b)" + }, + "description": "This dataset provides Global Navigation Satellite System (GNSS) Radio Occultation (RO) Excess Phase and Amplitude data from the GRACE (Gravity Recovery and Climate Experiment) mission as contributed by the University Corporation for Atmospheric Research. 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