United States of America climate and weather data

Dates of Images: 10/9/2025 - 10/30/2025 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) cloud masks were created by the NASA Black Marble Science team. This is the layer to displaying 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. 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. REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/gis05/services/DISASTERS_202510_FLOOD_AK/BM_BRDF_CLOUD/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.

EnviroAtlas image service of RCP85SpringTempMin for time slider

EnviroAtlas image service of RCP45AnnualPrecip for time slider

EnviroAtlas image service of RCP26SummerTempMax for time slider

BRDF_Helene_cloud

PRISM/PRISM_30yr_normals_maximum_temperature_degF_AK_1981_2010

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) ECO3ETALEXIU Version 1 data product provides estimates of daily evapotranspiration (ET) using the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product, along with ancillary meteorological data and remotely sensed vegetation cover information. The ECO3ETALEXIU data product is derived using a physics-based surface energy balance (SEB) algorithm, the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (disALEXI). Described in the Algorithm Theoretical Basis Document (ATBD), disALEXI is based on spatial disaggregation of regional-scale fluxes from the ALEXI SEB model. Many approaches exist for mapping ET spatially; however, SEB methods are favored for remote sensing retrievals based on land-surface temperature. ALEXI was initially developed for managed landscapes. Applications include crop water use, crop phenology monitoring, and drought early warning or water stress detection. The output ET is generated on a UTM grid at a spatial resolution of 30 meters. The ECO3ETALEXIU Version 1 data product contains layers of daily ET, ET uncertainty, and associated quality flags. 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 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.

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.