United States of America climate and weather data

EnviroAtlas image service of RCP60SummerPET for time slider

EnviroAtlas image service of RCP26AnnualTempMax for time slider

EnviroAtlas image service of RCP60WinterTempMax for time slider

EnviroAtlas image service of RCP45AnnualTempMin for time slider

This annual data was accessed from the PRISM project website (https://prism.oregonstate.edu/normals/) and has a spatial resolution of 30 arcsec (800 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original Metadata (https://prism.oregonstate.edu/normals/): Abstract: Monthly 30-year "normal" dataset covering the conterminous U.S., averaged over the climatological period 1991–2020. Contains spatially gridded average annual total precipitation and temperature at 4km grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website. Purpose: Display and/or analysis requiring spatially distributed annual total precipitation and temperature for the climatological period 1991–2020. Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data and an underlying grid such as a digital elevation model (DEM) or a 30 yr climatological average to generate gridded estimates of monthly or annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids were produced by averaging (temperatures, dew point, vapor pressure deficit, and solar radiation) or summing (precipitation) the monthly grids. These gridded normals supersede the 1991–2020 normals produced in October 2021. Improvements over the previous version include more stable adjustments of short-period station data to represent the 1991–2020 period, and additional quality control measures.

EnviroAtlas image service of RCP26SummerPrecip for time slider

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Gridded Evaporative Stress Index disALEXI 24-Hour L4 CONUS 70 m (ECO_L4G_ESI_ALEXI) Version 2 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented from the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD). ESI applications include indicating agricultural drought and observing vegetation stress. The ECO_L4G_ESI_ALEXI Version 2 data product is projected to a globally snapped 0.0006° grid with a 70 m spatial resolution over Conterminous United States (CONUS) and is distributed in HDF5. Each granule contains the variables of Evaporative Stress Index Daily and Evaporative Stress Index Daily Uncertainty. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). * During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * During the time period of January 1st, 2023, through September 30th, 2023, ECOSTRESS STARS and the resulting ET products are considered lower quality due to the priors used in the STARS model. The bug was corrected, and the lower quality data will be reprocessed. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The United States Department of Agriculture (USDA) ECO4ESIALEXIU Version 1 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented in Version 1 of the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD). ESI applications include indicating agricultural drought and observing vegetation stress. The dis-ALEXI USDA ESI product is generated on a UTM grid at a spatial resolution of 30 meters. The ECO4ESIALEXIU Version 1 data product contains variables of daily evaporative stress index, evaporative stress index uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.

EnviroAtlas image service of RCP26SummerTempMin for time slider

EnviroAtlas image service of HistWinterPET for time slider

PRISM/PRISM_30yr_normals_maximum_temperature_degF_AMSAM_1971_2000

PRISM/PRISM_30yr_normals_minimum_temperature_degF_AMSAM_1971_2000

The nested-Experimental Artificial Intelligence (AI) Global and Limited-area Ensemble (nested-EAGLE) is part of the NOAA EAGLE product suite that is designed to bridge the skill of short and medium range weather forecasts (see arXiv for details). It is based on the Anemoi framework developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and partner agencies, and is trained on the combination a 0.25° Global Forecast System (GFS) data and 6-km High-Resolution Rapid Refresh (HRRR) data over CONUS that produces 10-day forecasts for a range of atmospheric variables. This work was a joint effort between NOAA Research Laboratories and the Earth Prediction Innovation Center (EPIC), and the National Weather Service (NWS). Project EAGLE provides NOAA, the weather enterprise (including government, industry, and academia), and the broader community members with the ability to rapidly test, develop, and demonstrate AI models for global and limited-area ensemble forecasting. For more information, please visit the EPIC website, EAGLE Overview page or the EAGLE repository on GitHub. The nested-EAGLE data are available as netCDF files. It's offered on a free, unsupported basis. We do not recommend using it for any critical operational purposes. Domain Information The nested-EAGLE CONUS domain is at 6-km resolution. This data is on the HRRR Lambert Conformal Conic (LCC) grid. Global data at 0.25° is distributed in a separate collection (nested-eagle-global). Technical Specifications Archive length October 1st, 2024 - Present Update frequency This model generates forecasts in near-real-time and is initialized 4 times daily for 00, 06, 12, and 18 Z cycles. Forecasts are typically available 6 hours after initialization time. Forecast length Ten days (240 hours) with predictions every 6 hours Levels 2D: surface, 10 meter, and 80 meter 3D: 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, and 1000 hPa Specs The data are stored as netCDF files, one netCDF file per forecast. See the example Jupyter notebooks for guidance on how to load the data. License Information These data are in the worldwide public domain and are licensed under CC BY 4.0.How to Cite The Earth Prediction Innovation Center (EPIC) and the NOAA AI for Numerical Weather Prediction (AI4NWP) Working Group. (2026). The nested-EAGLE (Experimental AI Global and Limited-area Ensemble forecast system) v1.1.0 (Version v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.19672026

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.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Gridded Evapotranspiration disALEXI 24-hour L3 CONUS 70 m Version 2 data product provides estimates of daily evapotranspiration (ET). It utilizes the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product (ECO_L2T_LSTE.002), regional-scale fluxes from the Atmospheric Land Exchange Interface (ALEXI) model, and Normalized Difference Vegetation Index (NDVI) and albedo from the Spatial Timeseries for Automated high-Resolution multi-Sensor (STARS) data-fusion product (ECO_L2T_STARS.002), with meteorology sourced from the Climate Forecast System Reanalysis (CFSR). The ECO_L3G_ET_ALEXI data product is derived using a physics-based Two-Source Energy Balance (TSEB) model, implemented in the Atmosphere Land Exchange Inverse (ALEXI) disaggregation algorithm (DisALEXI). Described in the Algorithm Theoretical Basis Document (ATBD), DisALEXI spatially disaggregates the regional ET produced by the ALEXI TSEB model. While there are many approaches for spatially mapping ET, models based on the surface energy balance are favored for remote sensing retrievals based on land-surface temperature. Applications include estimating crop-water use, phenology monitoring, and drought early-warning or vegetation-water-stress detection. The ECO_L3G_ET_ALEXI Version 2 data product is projected to a globally snapped 0.0006° grid with a 70 m spatial resolution over Conterminous United States (CONUS) and is distributed in HDF5. Each granule contains variables of Evapotranspiration Daily and Evapotranspiration Daily Uncertainty. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). * During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * During the time period of January 1st, 2023, through September 30th, 2023, ECOSTRESS STARS and the resulting ET products are considered lower quality due to the priors used in the STARS model. The bug was corrected, and the lower quality data will be reprocessed. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

PRISM/PRISM_30yr_normals_precipitation_inches_AMSAM_1971_2000

This annual data was accessed from the PRISM project website (https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 3 arcsec (80 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. For this project, Guam and CNMI domains were merged into one spatial domain using the "Mosaic to New Raster" tool in ArcGIS Pro. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Tiled Evapotranspiration disALEXI 24-hour L3 CONUS 70 m Version 2 data product provides estimates of daily evapotranspiration (ET). It utilizes the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product (ECO_L2T_LSTE.002), regional-scale fluxes from the Atmospheric Land Exchange Interface (ALEXI) model, and Normalized Difference Vegetation Index (NDVI) and albedo from the Spatial Timeseries for Automated high-Resolution multi-Sensor (STARS) data-fusion product (ECO_L2T_STARS.002), with meteorology sourced from the Climate Forecast System Reanalysis (CFSR). The ECO_L3T_ET_ALEXI data product is derived using a physics-based Two-Source Energy Balance (TSEB) model, implemented in the Atmosphere Land Exchange Inverse (ALEXI) disaggregation algorithm (DisALEXI). Described in the Algorithm Theoretical Basis Document (ATBD), DisALEXI spatially disaggregates the regional ET produced by the ALEXI TSEB model. While there are many approaches for spatially mapping ET, models based on the surface energy balance are favored for remote sensing retrievals based on land-surface temperature. Applications include estimating crop water use, phenology monitoring, and drought early warning or detecting vegetation water stress. The ECO_L3T_ET_ALEXI Version 2 data product is available in Cloud Optimized GeoTIFF (COG) format for Conterminous United States (CONUS) using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. Each granule contains a separate COG file for each variable: Evapotranspiration Daily and Evapotranspiration Daily Uncertainty. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). * During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * During the time period of January 1st, 2023, through September 30th, 2023, ECOSTRESS STARS and the resulting ET products are considered lower quality due to the priors used in the STARS model. The bug was corrected, and the lower quality data will be reprocessed. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

Rasters/CMAQ

Dates of Images: Post-Event: January 26, 2026; January 27, 2026; January 28, 2026; January 29, 2026 Pre-Event: December 2025 (composite) Please Note: Images with majority cloud cover are not shown in the Map Viewer, but are available in the service. Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. Additional cloud cover layers are included to indicate where clouds are present. The baseline image is from September 2025, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/202601_BlackMarble_BRDF_CloudMask/ImageServer/WMSServer

PRISM/PRISM_30yr_normals_minimum_temperature_degF_AK_1981_2010

This annual data was accessed from the PRISM project website (https://https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 15 arcsec (400 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

This annual data was accessed from the PRISM project website (https://https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 15 arcsec (400 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

This annual data was accessed from the PRISM project website (https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 3 arcsec (80 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. For this project, Guam and CNMI domains were merged into one spatial domain using the "Mosaic to New Raster" tool in ArcGIS Pro. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

PRISM/PRISM_30yr_normals_precipitation_inches_AK_1981_2010

This annual data was accessed from the PRISM project website (https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 3 arcsec (80 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. For this project, Guam and CNMI domains were merged into one spatial domain using the "Mosaic to New Raster" tool in ArcGIS Pro. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

This annual data was accessed from the PRISM project website (https://prism.oregonstate.edu/normals/) and has a spatial resolution of 30 arcsec (800 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original Metadata (https://prism.oregonstate.edu/normals/): Abstract: Monthly 30-year "normal" dataset covering the conterminous U.S., averaged over the climatological period 1991–2020. Contains spatially gridded average annual total precipitation and temperature at 4km grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website. Purpose: Display and/or analysis requiring spatially distributed annual total precipitation and temperature for the climatological period 1991–2020. Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data and an underlying grid such as a digital elevation model (DEM) or a 30 yr climatological average to generate gridded estimates of monthly or annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids were produced by averaging (temperatures, dew point, vapor pressure deficit, and solar radiation) or summing (precipitation) the monthly grids. These gridded normals supersede the 1991–2020 normals produced in October 2021. Improvements over the previous version include more stable adjustments of short-period station data to represent the 1991–2020 period, and additional quality control measures.

Date of Images: Post-Event: 4/8, 4/7 Pre-Event: 3/28, 3/24, 3/21, 3/18 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space and may show damage caused by severe weather. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Satellite/Sensor: PlanetScope. This product includes copyrighted material of Planet Labs PBC. All rights reserved. Resolution: 3 Meters Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=91b8b7142b1a49d0a264da80e3f85b35 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202504_SEVEREWX_SOUTHEASTUS/2505_planet/ImageServer/WMSServer

This annual data was accessed from the PRISM project website (https://https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 15 arcsec (400 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

This annual data was accessed from the PRISM project website (https://https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 15 arcsec (400 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

EnviroAtlas image service of RCP60SpringPET for time slider

EnviroAtlas image service of RCP85WinterPrecip for time slider

EnviroAtlas image service of RCP45AnnualPET for time slider

This annual data was accessed from the PRISM project website (https://https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 15 arcsec (400 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website <http://www.ocs.oregonstate.edu/prism/> Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC). All COOP, SNOTEL and other data were subjected to further quality control checks by the PRISM Group.

EnviroAtlas image service of RCP60SpringTempMin for time slider

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Tiled Evaporative Stress Index disALEXI 24-Hour L4 CONUS 70 m (ECO_L4T_ESI_ALEXI) Version 2 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented from the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD). ESI applications include indicating agricultural drought and observing vegetation stress. The ECO_L4T_ESI_ALEXI Version 2 data product is available in Cloud Optimized GeoTIFF (COG) format for Conterminous United States (CONUS) using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. Each granule contains a separate COG file for each variable: Evapotranspiration Daily and Evapotranspiration Daily Uncertainty. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). * During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * During the time period of January 1st, 2023, through September 30th, 2023, ECOSTRESS STARS and the resulting ET products are considered lower quality due to the priors used in the STARS model. The bug was corrected, and the lower quality data will be reprocessed. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.