United States of America climate and weather data

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Weather observation points from the Kentucky Mesonet, with each feature representing a station and its readings such as air temperature, relative humidity, dewpoint, wind speed and direction, solar radiation and precipitation. Records include station ID, county, state, latitude, longitude, elevation and observation times. Published by Kentucky_DGI.

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June, July, and August 2020-2023 for the area covering a city of interest and with less than 20% cloud cover. Pixels converted into points and later summarized by NPA to calculate mean, min, and max.

Dates of Images: May 18 Summary Surface concentrations of PM2.5 Level 4 (L4) files were generated from Tropospheric Emissions: Monitoring of Pollution (TEMPO) Satellite and Advanced Baseline Imager (ABI) aerosol retrievals. The aerosol optical depth (AOD) and PM2.5 products were created at NOAA/NESDIS. The data covers the daytime observation windows during the active fire period from May 18, 2026. Because TEMPO relies on sunlight, these observations represent approximately 10 to 12 hours of sun-lit coverage per calendar day rather than a full 24-hour cycle. For emergency managers, this highlights total cumulative exposure durations, allowing officials to efficiently target health impacts in heavily affected communities. Caveats and Limitations Since the TEMPO instrument only makes observations during daylight hours, communities likely experienced significant additional nighttime smoke exposure that is not captured in these products. Furthermore, dense cloud cover can block satellite measurements, and these values may underestimate the true duration of smoke exposure in persistently cloudy areas. TEMPO data also exhibits more noise at the edges of clouds. Suggested Use While hours when surface PM2.5 was greater than 35 µg/m³ represents the health standard referred to as "Unhealthy for Sensitive Groups," this daytime-only product is developed only for rapid disaster response and should not be used for official EPA regulatory compliance, which requires a full 24-hour average. Satellite/Sensor/Resolution The surface PM2.5 product is estimated using data from two primary instruments: the TEMPO spectrometer aboard the Intelsat-40e commercial satellite and the Advanced Baseline Imager (ABI) on the GOES-R Series satellites (GOES-East and GOES-West). The final product is derived using a geographically weighted regression (GWR) algorithm that combines satellite aerosol optical depth (AOD) and aerosol layer height (ALH) with hourly data from AIRNOW surface monitors. While the raw TEMPO data has a native resolution of 2.0 km by 4.75 km, it is re-scaled to match the ABI's fixed grid, resulting in a final product with a spatial resolution of 2 km. Credits AOD and PM2.5 were provided by NOAA/NESDIS. Data analysis and cumulative exposure maps were created by Dr. Aishwarya Raman (BAERI/NASA ARC). Point of contact for TEMPO PM2.5 data - Amy Huff (Science and Technology Corporation (STC) at NOAA) and Shobha Kondragunta (NOAA). Esri REST Endpoint: See URL to the right. Data Download For data, please contact Amy Huff (Science and Technology Corporation (STC) at NOAA) and Shobha Kondragunta (NOAA).

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Date of Images: 01/07/2025, 01/10/2025 Summary: Land surface temperature is the heat you would feel as you touch the ground, rooftop, or other surface. Certain materials retain more heat than others – for instance, beach sand is warmer than vegetated regions. Very recently burned areas will maintain some residual heat, and active fires will have very high heat signatures. Water holds onto heat for longer than land, so the ocean is warmer than the land at night and cooler than the land during the day. 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 as well as key biomes and agricultural zones around the world and selected FLUXNET validation sites. A map of the acquisition coverage can be found on the ECOSTRESS website. The ECO2LSTE Version 2 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO2LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. Satellite/Sensor: ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) on the International Space Station (ISS) Resolution: 70 meters Credits: NASA/Jet Propulsion Laboratory, California Institute of Technology, International Space Station (ISS) For More Information: ECOSTRESS Project at JPL: https://ecostress.jpl.nasa.gov/ Hook, S., Hulley, G. (2019). ECOSTRESS Land Surface Temperature and Emissivity Daily L2 Global 70 m V001 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2023-08-15 from https://doi.org/10.5067/ECOSTRESS/ECO2LSTE.001 Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=04cde7850ceb45178e33fc049c9071ee WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_ecostress/ImageServer/WMSServer

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

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/DISASTERS_202601_WINTERWX_US/202601_BlackMarble_BRDF/ImageServer/WMSServer

Note: this data is currently in testing and is experimental. Date of Images: Post-Event: 4/2/2025-4/6/2025 Summary: Storms that produce heavy rain, damaging winds, hail, and tornadoes often exhibit distinct cloud-top patterns in satellite imagery. Two of these patterns, overshooting tops (OTs) and above-anvil cirrus plumes (AACPs), are frequently observed over intense storms. Using deep learning techniques, researchers at NASA Langley Research Center developed software to automatically detect these severe storm signatures in 500m GOES satellite data collected every 5 minutes. Emergency managers can use this raster data to quickly identify locations where severe storms likely occurred, complementing ground-based reports in regions with limited observations. Severe weather on the ground is more likely when OTs and AACPs are both detected and in areas where detections are clustered or observed repeatedly. Long, nearly straight lines of OT and AACP time-aggregated detections can indicate severe storm tracks. Suggested Use and Use Cases: Target audiences: GIS specialists, tactical emergency managers Information Needed: Near real-time situational awareness about locations where severe storm impacts may have occurred. Example Uses: Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation). Value Proposition: Low resolution, low latency data shows areas that may have recently or may soon experience severe weather (nowcasting). Satellite: Geostationary Operational Environmental Satellite (GOES) Satellite remote sensing over North & South America Resolution: 5 minute temporal resolution observation frequency, 500m spatial resolution Product Specifications: Caveats: While the presence of OTs and AACPs often correlates with severe weather, this product does not observe ground impacts and may detect features that do not correspond to severe weather. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=b1dbafac653b41e8b4716de1020a201e WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202504_SEVEREWX_SOUTHEASTUS/2505_goes/ImageServer/WMSServer

EnviroAtlas image service of HistSpringPrecip for time slider

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EnviroAtlas image service of HistWinterTempMax for time slider

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Dates of Images: August 10, 2026 - August 12, 2026 Summary: Storms that produce heavy rain, damaging winds, hail, and tornadoes often exhibit distinct cloud-top patterns in satellite imagery. Two of these patterns, overshooting tops (OTs) and above-anvil cirrus plumes (AACPs), are frequently observed over intense storms. Using deep learning techniques, researchers at NASA Langley Research Center developed software to automatically detect these severe storm signatures in 500m GOES satellite data collected every 5 minutes. Emergency managers can use this raster data to quickly identify locations where severe storms likely occurred, complementing ground-based reports in regions with limited observations. Severe weather on the ground is more likely when OTs and AACPs are both detected and in areas where detections are clustered or observed repeatedly. Long, nearly straight lines of OT and AACP time-aggregated detections can indicate severe storm tracks. Suggested Use: Near real-time situational awareness about locations where severe storm impacts may have occurred. Example Uses: Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation). Satellite/Sensor/Resolution: GOES-R satellite channels 13 and 15 at approximately 2 km resolution and 5-10 mins. There were some issues with some of the cases GOES files so there are times in which they were skipped. GFS tropopause temperature was also used. Data are all interpolated to 0.5 km resolution. Credits: If you use this software in your project, please contact our team prior to publication and cite our contribution. When using this software, credit the technical background and validation for this software as described here: Cooney, J. W., Bedka, K. M., Liles, C. A., and Homeyer, C. R. (in review). Automated Detection of Overshooting Tops and Above Anvil Cirrus Plumes Within Geostationary Imagery Using Deep Learning. Artificial Intelligence for the Earth Systems. Code is available at https://github.com/nasa/svrstormsig. Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/rest/services/DISASTERS_202608_FLOOD_OH/Severe_Storm_Signatures_Ohio_Flooding_August_2026/MapServer

Dates of Images: September 13, 2018-September 21, 2018 Summary: GPM IMERG Final Precipitation Rate L3 V07(GPM IMERG Final Precipitation L3 Daily 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF V07))is an image service derived from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) Early dataset. The web service shows total precipitation (mm) for the date range outlined by aggregating 30-minute intervals. This data is shown as cumulative (from start date) and on a daily basis. IMERG is an algorithm that estimates precipitation rate from multiple passive microwave sensors in the GPM constellation, the GPM Dual-Frequency Radar, and infrared (IR) sensors mounted on geostationary satellites. Currently, the near-real-time Early estimates have no concluding calibration. Briefly describing the Early Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the Combined Radar-Radiometer Algorithm (CORRA) product (because it is presumed to be the best snapshot Tropical Rainfall Measuring Mission (TRMM)/GPM estimate after adjustment to the monthly Global Precipitation Climatology Project Satellite-Gauge (GPCP SG)), then "forward morphed" and combined with microwave precipitation-calibrated geo-IR fields to provide half-hourly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is computed using analyses of surface temperature, humidity, and pressure. Credits: Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2024), GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date], 10.5067/GPM/IMERG/3B-HH-E/07 Data Access: This data was accessed using NASA Earthdata with a sample URL as follows:https://search.earthdata.nasa.gov/search/granules/collection-details?p=C2723754864-GES_DISC&pg[0][v]=f&pg[0][gsk]=-start_date&q=GPM Dataset at a Glance Shortname: GPM_3IMERGHHL DOI:10.5067/GPM/IMERG/3B-HH-E/07 Version: 07 Coverage: -180.0,-90.0,180.0,90.0 Temporal Coverage: 1998-01-01 to Present Data Resolution Spatial: 0.1 ° x 0.1 ° Temporal: 30 minutes Esri REST Endpoint: See Layers below. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_gpm/MapServer/WMSServer

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Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web service depict ecosystem services at the census block group level for all EnviroAtlas communities. These layers illustrate the ecosystems and natural resources that are associated with clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, and aesthetics, and biodiversity conservation, and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

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