Climate and weather data

EnviroAtlas image service of HistSummerTempMin for time slider

sam_seas_full_ir_tropopause_difference_99th_percentile

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.

This collection offers 48 parameters at near-surface level from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes. ## Data collection height There are 3 forecast heights used within the near-surface this collection: * Surface: the default collection height * Screen level: 1.5m above the surface * Wind parameters: 10m above the surface ## Timesteps For most parameters, the following time steps are available, see exceptions below: * every hour from 0 to 54 hours * every 3 hours from 57 to 144 hours * every 6 hours from 150 to 168 hours Exceptions (for `accumulation`, `min`, `max` and `mean` parameters): * Height of orography (height_of_orography) is only available at 0H * Hourly latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT01H) is only available every hour from 1 to 54 hours * 3H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT03H) is only available every three hours from 57 to 144 hours * 6H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT06H) is only available every six hours from 150 to 168 hours * Hourly precipitation accumulation (precipitation_accumulation-PT01H) is only available every hour from 1 to 54 hours * 3H precipitation accumulation (precipitation_accumulation-PT03H) is only available every three hours from 57 to 144 hours * 6H precipitation accumulation (precipitation_accumulation-PT06H) is only available every six hours from 150 to 168 hours * Radiation flux in longwave downward at surface (radiation_flux_in_longwave_downward_at_surface) has six hourly timesteps from 150 to 162 hours * Radiation flux in uv downward at surface (radiation_flux_in_uv_downward_at_surface) has three hourly timesteps from 0 to 144 hours and six hourly timesteps from 150 to 162 hours * Hourly rainfall accumulation (rainfall_accumulation-PT01H) is only available every hour from 1 to 54 hours * 3H rainfall accumulation (rainfall_accumulation-PT03H) is only available every three hours from 57 to 144 hours * 6H rainfall accumulation (rainfall_accumulation-PT06H) is only available every six hours from 150 to 168 hours * Hourly rainfall rate from convection max (rainfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours * 3H rainfall rate from convection max (rainfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours * 6H rainfall rate from convection max (rainfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours * Hourly snowfall rate from convection max (snowfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours * 3H snowfall rate from convection max (snowfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours * 6H snowfall rate from convection max (snowfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours * Hourly snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT01H) is only available every hour from 1 to 54 hours * 3H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT03H) is only available every three hours from 57 to 144 hours * 6H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT06H) is only available every six hours from 150 to 168 hours * Hourly temperature at screen level max (temperature_at_screen_level_max-PT01H) is only available every hour from 1 to 54 hours * 3H temperature at screen level max (temperature_at_screen_level_max-PT03H) is only available every three hours from 57 to 144 hours * 6H temperature at screen level max (temperature_at_screen_level_max-PT06H) is only available every six hours from 150 to 168 hours * Hourly temperature at screen level min (temperature_at_screen_level_min-PT01H) is only available every hour from 1 to 54 hours * 3H remperature at screen level min (temperature_at_screen_level_min-PT03H) is only available every three hours from 57 to 144 hours * 6H temperature at screen level min (temperature_at_screen_level_min-PT06H) is only available every six hours from 150 to 168 hours * Hourly wind gust at 10m max (wind_gust_at_10m_max-PT01H) is only available every hour from 1 to 54 hours * 3H wind gust at 10m max (wind_gust_at_10m_max-PT03H) is only available every three hours from 3 to 144 hours * 6H wind gust at 10m max (wind_gust_at_10m_max-PT06H) is only available every six hours from 54 to 168 hours ## Update Frequency The model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC). The runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours. The forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available. ## Archive length and latency As of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. The data is typically available 6 hours after the model run time. ## Technical specs The data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: * Variables store the data * Dimensions give relevant dimension information for the variables * Attributes provide auxiliary information about the variables or dataset itself NetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers. Iris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. Find further support on using Iris with NetCDF files. ## Help us improve the data services we offer Join the Met Office research panel to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities. ## How to cite Met Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). ## License British Crown copyright 2023-2025, the Met Office, is licensed under CC BY-SA. ## Providers Met Office See all datasets managed by Met Office. ## Contact [servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.

Monthly counts of anvil cloud days per month over South America, derived from infrared satellite observations. Published by mstisdal.

South Africa Climatology Full 2005 2019 IR Tropopause Difference 99th Percentile

ArcGIS Image Service Time Interval: Annual With Hourly TimestepsVersion: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (With 24 Hours) Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African Anvil Cloud Days per Year detection climatological statistics averaged Annually with Hourly

saf_monthly_ir_tropopause_difference_99th_percentile

sam_clim_hr_ir_tropopause_difference_99th_percentile

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).

South Africa Seasonal Full 2005-2019 IR Anvil Day Count

ArcGIS Image ServiceTime Interval: Full Climatology Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African Anvil Cloud Days per Year detection climatological statistics averaged over 2005-2025

South America Climatology Hourly IR Anvil Hour Count

sam_seas_hr_1995_2021_cloud_top_height_99th_percentile

This Collection provides Sentinel-5P Level-2 O3 PR products, which contains ozone concentration for 33 levels in the atmosphere.

ArcGIS Image ServiceTime Interval: Annual With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (With 24 Hours) Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African OT detection climatological statistics averaged Annually with Hourly Timesteps.

EnviroAtlas image service of HistFallPrecip for time slider

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents the maximum temperature of the warmest month across North America for the 1981–2010 climatological reference period, in degrees Celsius. The dataset shows the highest monthly mean of daily maximum temperatures across the year, highlighting peak thermal conditions, and is derived from a kilometer-scale climate product generated using the CHELSA downscaling model, representing long-term climatological means. The North American maximum temperature layer was derived from the CHELSA v2.1 bioclimatic variables (BIO5) dataset, which is based on statistically downscaled temperature data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a mechanistic, terrain-informed downscaling framework that accounts for orographic effects, atmospheric lapse rates, and wind-driven processes to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., & Karger, D. N. (2022). Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14(12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Brun et al. (2022). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial patterns of peak monthly temperature across North America. Supporting heat stress, drought, and climate risk assessment. Informing agricultural, ecological, and species distribution modeling. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

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

South Africa Seasonal Full 2005-2019 IR OT Day Count

Hourly counts of infrared-detected anvil clouds, aggregated into annual climatology with hourly timesteps, for South Africa. Published by mstisdal.

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of August across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), shows the total precipitation recorded during the wettest month across North America for the 1981–2010 climatological reference period, in millimeters. The dataset is derived from the CHELSA v2.1 bioclimatic variables dataset (BIO13) by identifying, for each pixel, the maximum monthly precipitation value within the twelve monthly precipitation layers. The North American climate data were derived from the CHELSA v2.1 bioclimatic variables dataset, which is produced using a mechanistic, terrain-informed climate downscaling framework that generates high-resolution gridded temperature, precipitation, and related climate variables. Additional technical details on the downscaling methods and derived climate predictors are provided in: Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., & Karger, D. N. (2022). Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14(12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Brun et al. (2022). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial patterns of peak monthly precipitation extremes across North America. Supporting flood risk assessment, water-resource, and infrastructure planning. Informing ecological and climate impact modeling. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Annual climatology of 99th percentile cloud top height at hourly timesteps, part of a South Africa convection climatology. Published by mstisdal.

saf_ann_full_cloud_top_height_99th_percentile properties

The goal of the updated map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. An updated timestamp (2022 vs. 2016) and finer grained resolution (10m vs. 30m) improves the utility of the data for planning purposes. The LST dataset from 2016 can be accessed via the Minnesota Geospatial Commons. High resolution (10 meter) land surface temperature (LST) from September 1, 2022 is mapped for the seven-county metropolitan region of the Twin Cities. The goal of the map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. The raster dataset was computed at 30 meters using satellite imagery from Landsat 9 and downscaled to 10 meters using Copernicus Sentinel-2. These datasets were integrated using techniques modified from Ermida et al. 2020 and Onačillová et al. 2022). Open water was removed using ancillary data from OpenStreetMap and 2020 Generalized Land Use for the Twin Cities (Metropolitan Council).  First, Landsat 9 imagery taken at 11:59 am CDT on September 01, 2022 was processed into 30-meter resolution LST (based on Ermida et al. 2020). At this time, the air temperature was 88° F at the Minneapolis-St. Paul International Airport (NOAA). A model predicting LST based on spectral indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) was created and applied to 10-meter Sentenel-2 imagery. Sentinel-2 imagery was also taken on September 1, 2022, and this resulted in a 10-meter downscaled LST image (based on Onačillová et al. 2022). To account for anomalies in NDVI on the primary image date of September 1 (e.g., recently harvested agricultural fields), maximum NDVI occurring between July 1, 2022 and September 1, 2022 was used for both Landsat and Sentinel image processing. Water bodies were removed for all processing steps (OpenStreetMap 2023, Metropolitan Council 2021).  This dataset is an update to the 2016 LST data for the Twin Cities Region (Metropolitan Council).  The code to create and processes this dataset is available at: https://github.com/Metropolitan-Council/extreme.heat  Sources:  Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471.  Metropolitan Council. 2021. Generalized Land Use 2020. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-generl-lnduse2020  Metropolitan Council. 2017. Land Surface Temperature for Climate Vulnerability Analysis. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-env-cva-lst2016  NOAA, National Oceanic and Atmospheric Administration, National Centers for Environmental Information, station USW00014922. September 1, 2022.  Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022. Combining Landsat 8 and Sentinel 2 data in Google Earth Engine to derive higher resolution land surface temperature maps in urban environment. Remote Sensing, 14 (16), 4076. https://doi.org/10.3390/rs14164076.  OpenStreetMap contributors. 2023. Retrieved from https://planet.openstreetmap.org on April 12, 2023.

This raster records the 99th percentile cloud top height as an annual climatology with hourly timesteps for South America convection. It is published by mstisdal.

ArcGIS Image ServiceTime Interval: Monthly Version: 1 Spatial Resolution: 3 km Time Extent: 2005-01-15 to 2025-12-15 Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis service provides monthly gridded overshooting cloud top detection, specifically the Overshooting Top Days/Month over South Africa.

South Africa Climatology Full 2005 2019 IR Anvile Day Count

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

The Cooperative Open Online Landslide Repository (COOLR) contains citizen science data from Landslide Reporter put into the database as Landslide Reporter Catalog (event_import_source = ‘LRC’) and data from NASA’s Global Landslide Catalog (event_import_source = ‘GLC’) compiled since 2007. Background The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall and some triggered by other events, that have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. The Cooperative Open Online Landslide Repository (COOLR) is a worldwide database of landslide events. It currently includes NASA’s Global Landslide Catalog (GLC) and other landslide reports contributed by citizen scientists. In the future, other landslide inventories will be added to COOLR. Methods We use 'landslide' as a general term to represent all event types. The catalog differentiates between landslide types when the information is available in the landslide_category field. COOLR follows a similar methodology to the GLC, and its fields and other information are documented in the publications below. Landslide events in the GLC were obtained from online news media, links to reported landslides in newspapers, hazard databases, scientific reports, blog entries, and personal communication and other sources. Online news articles serve as the primary sources of information for the landslide catalog and are found using Google Alerts (www.google.com/alerts) and other search engine options. Citizen science points submitted to COOLR through Landslide Reporter are checked for accuracy compared to the source material, accuracy of location and landslide identification, and the quality of the description provided. The source of the information in all COOLR points and polygons are described in the individual objects, which can be found in the source_name and source_link columns. For Further Reference landslides.nasa.gov Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:10.1016/j.geomorph.2015.03.016 Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:10.1007/s11069-009-9401-4

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

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC). --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- This annual meteorology service provides time-enabled global Analysis Ready Data (ARD) parameters from 1981 to 2023 for POWER’s communities.   Time Interval: Annual Time Extent: 1981/01/01 to 2023/12/31 Time Standard: Local Sidereal Time (LST) Grid Size: 0.5 x 0.5 Degree Projection: GCS WGS84 Extent: Global Source: NASA Prediction Of Worldwide Energy Resources (POWER) For questions or issues please email: larc-power-project@mail.nasa.gov Meteorology Data Sources: NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – Dec. 31, 2023) Meteorology Data Parameters: CDD10 (Cooling Degree Days Above 10 C): The daily accumulation of degrees when the daily mean temperature is above 10 degrees Celsius. CDD18_3 (Cooling Degree Days Above 18.3 C): The daily accumulation of degrees when the daily mean temperature is above 18.3 degrees Celsius. DISPH (Zero Plane Displacement Height): The height at which the mean velocity is zero due to large obstacles such as buildings/canopy. EVLAND (Evaporation Land): The evaporation over land at the surface of the earth. EVPTRNS (Evapotranspiration Energy Flux): The evapotranspiration energy flux at the surface of the earth. FROST_DAYS (Frost Days): A frost day occurs when the 2m temperature cools to the dew point temperature and both are less than 0 C or 32 F. GWETTOP (Surface Soil Wetness): The percent of soil moisture a value of 0 indicates a completely water-free soil and a value of 1 indicates a completely saturated soil; where surface is the layer from the surface 0 cm to 5 cm below grade. HDD10 (Heating Degree Days Below 10 C): The daily accumulation of degrees when the daily mean temperature is below 10 degrees Celsius. HDD18_3 (Heating Degree Days Below 18.3 C): The daily accumulation of degrees when the daily mean temperature is below 15.3 degrees Celsius. PBLTOP (Planetary Boundary Layer Top Pressure): The pressure at the top of the planet boundary layer. PRECSNOLAND_SUM (Snow Precipitation Land Sum): The snow precipitation sum over land at the surface of the earth. PRECTOTCORR_SUM (Precipitation Corrected Sum): The bias corrected sum of total precipitation at the surface of the earth. PS (Surface Pressure): The average of surface pressure at the surface of the earth. QV10M (Specific Humidity at 10 Meters): The ratio of the mass of water vapor to the total mass of air at 10 meters (kg water/kg total air). QV2M (Specific Humidity at 2 Meters): The ratio of the mass of water vapor to the total mass of air at 2 meters (kg water/kg total air). RH2M (Relative Humidity at 2 Meters): The ratio of actual partial pressure of water vapor to the partial pressure at saturation, expressed in percent. T10M (Temperature at 10 Meters): The air (dry bulb) temperature at 10 meters above the surface of the earth. T2M (Temperature at 2 Meters): The average air (dry bulb) temperature at 2 meters above the surface of the earth. T2MDEW (Dew/Frost Point at 2 Meters): The dew/frost point temperature at 2 meters above the surface of the earth. T2MWET (Wet Bulb Temperature at 2 Meters): The adiabatic saturation temperature which can be measured by a thermometer covered in a water-soaked cloth over which air is passed at 2 meters above the surface of the earth. TO3 (Total Column Ozone): The total amount of ozone in a column extending vertically from the earth's surface to the top of the atmosphere. TQV (Total Column Precipitable Water): The total atmospheric water vapor contained in a vertical column of unit cross-sectional area extending from the surface to the top of the atmosphere. TS (Earth Skin Temperature): The average temperature at the earth's surface. WD10M (Wind Direction at 10 Meters): The average of the wind direction at 10 meters above the surface of the earth. WD2M (Wind Direction at 2 Meters): The average of the wind direction at 2 meters above the surface of the earth. WD50M (Wind Direction at 50 Meters): The average of the wind direction at 50 meters above the surface of the earth. WS10M (Wind Speed at 10 Meters): The average of wind speed at 10 meters above the surface of the earth. WS2M (Wind Speed at 2 Meters): The average of wind speed at 2 meters above the surface of the earth. WS50M (Wind Speed at 50 Meters): The average of wind speed at 50 meters above the surface of the earth.

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