United States of America climate and weather data

EnviroAtlas image service of RCP85SummerPET for time slider

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EnviroAtlas image service of RCP26FallPET for time slider

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Dates of Images: October 9-October 11, 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

EnviroAtlas image service of RCP45WinterTempMax for time slider

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EnviroAtlas image service of RCP85SummerPrecip for time slider

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This raster records PM2.5 exceedance based on TEMPO data for the United States. It is published by patrick_rea.

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EnviroAtlas image service of HistFallPET for time slider

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).

Dates of Images: September 14, 2018-September 20, 2018 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/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_severestorm/MapServer/WMSServer

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

EnviroAtlas image service of RCP85AnnualPET for time slider

EnviroAtlas image service of RCP60FallTempMin for time slider

EnviroAtlas image service of RCP45SummerPrecip for time slider

EnviroAtlas image service of RCP85WinterTempMin for time slider

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Dates of Images: May 18- 25, 2026 Summary This product provides the PM2.5 impacts on air at the surface from the Sandy Fire by calculating the total number of daytime hours when surface-air fine particulate matter (PM2.5) exceeded the 35 µg/m³ health threshold for sensitive groups (USG). 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), an intermediate data product input for this analysis, and PM2.5 products were created at NOAA/NESDIS. The data covers the daytime observation windows during the active fire period from May 18 to May 25, 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. Red colors in the image represent more hours when the surface pM2.5 concentrations were higher than 35 µg/m³. Satellite/Sensor/Resolution The estimated surface PM2.5 product is generated 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 was 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). Data Download For data, please contact Amy Huff (Science and Technology Corporation (STC) at NOAA) and Shobha Kondragunta (NOAA).

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Dataset is comprised of hourly air quality data points captured from the District’s air monitoring network sites. The dataset is quality controlled by a process of field data verification and validation. The District of Columbia is providing this data for a rolling 1-year period. For complete real-time data, visit the EPA AirNow system at docs.airnowapi.org

EnviroAtlas image service of RCP45SpringPrecip for time slider

EnviroAtlas image service of RCP26WinterTempMin for time slider

EnviroAtlas image service of RCP45FallPET for time slider

Dates of Images: September 14, 2018-September 20, 2018 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/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_severestorm/MapServer/WMSServer