Climate and weather data

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of February 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

South America Seasonal Full 1995 2021 IR Anvil Day Count

sam_seas_full_1995_2021_cloud_top_height_99th_percentile

South Africa Climatology Hourly 2005-2019 IR Anvil Hour 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 CTH detection climatological statistics averaged over 2005-2025

smash_trmm_normalized_climatology_norm_hail_density

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 monthly meteorology service provides time-enabled global Analysis Ready Data (ARD) parameters from 1981 to 2023 for POWER’s communities.   Time Interval: Monthly 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, 2021) 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.

Displays the average of all measured values of a sensor in the last 5 minutes The displayed measured values were filtered to high and low outliers. - High outliers are everything beyond the 3. Quartils + 1.5* of the Inter-Quartils Range (IQB) - Low outliers are all below 1. Quartils - 1.5 * IQB

South America Climatology Full 1995-2021 Cloud Top Height 99th Percentile

EnviroAtlas image service of HistFallTempMax for time slider

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 IR detection climatological statistics averaged over 2005-2025

ArcGIS Image ServiceTime Interval: Seasonal Climatology Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) 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: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2005-2025

South America Monthly Cloud Top Height 99th Percentile

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents the month of August within a dataset of 12 raster files (one for each month) representing total precipitation values across North America for the 1981–2010 climatological reference period, in millimeters. The dataset is derived from a kilometer-scale climate product generated using the CHELSA downscaling model and represents long-term climatological means. The North American monthly precipitation 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 precipitation 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 precipitation 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

South Africa Seasonal Hourly 2005 2019 IR Anvile Hour Count

This collection offers 8 parameters at 33 available pressure levels (from 100000Pa to 1000Pa) 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. ## Pressure Levels Available pressure levels, in Pascals (Pa), are: * 100000.0 97500.0 95000.0 92500.0 90000.0 85000.0 80000.0 75000.0 70000.0 65000.0 60000.0 55000.0 50000.0 45000.0 40000.0 37500.0 35000.0 32500.0 30000.0 27500.0 25000.0 22500.0 20000.0 17500.0 15000.0 12500.0 10000.0 7000.0 5000.0 4000.0 3000.0 2000.0 1000.0 500.0 200.0 100.0 40.0 ## Timesteps The following timesteps are available: * every hour from 0 to 54 hours (for most parameters, see parameter table for exceptions) * every 3 hours from 57 to 144 hours * every 6 hours from 150 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.

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

Canadians have long served their country by participating in numerous wars and conflicts. Over the years, individual citizens, veterans organizations and service clubs have raised thousands of memorials across the country to honour those sailors, soldiers, airmen and women who made great sacrifices towards the restoration of world peace and served their country so well. In keeping with our proud military heritage, Canadians erected cenotaphs in municipal centres, raised monuments in parks, cemeteries and public areas. Church members have mounted plaques and stained glass windows as memorials, and cairns and fountains have been constructed as important landmarks marking various military involvement. All of this is done to mark the service of family, friends and comrades, and to ensure that these important and not so well known battles and events form part of our communal memory. Visit Memorials - Remembrance - Veterans Affairs Canada for memorials and war cemeteries in Canada and around the world that honour Canadians who have served. Source: National Inventory of Canadian Military Memorials Update Frequency: As needed Contact Information:For inquiries regarding the data please contact Veterans Affairs Canada (memorial-memoriaux@veterans.gc.ca) For inquiries regarding the service, please leave a comment below French version can be found here

sam_annual_hr_ir_tropopause_difference_99th_percentile

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 OT detection climatological statistics averaged over 2005-2025

This collection offers 4 parameters at 33 available height levels (5m to 6000m) from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files. ## Height Levels Available height levels, in metres (m) above ground, are: * 5.0 10.0 20.0 30.0 40.0 50.0 60.0 75.0 100.0 125.0 150.0 175.0 200.0 225.0 250.0 275.0 300.0 350.0 400.0 450.0 500.0 600.0 700.0 800.0 900.0 1000.0 1125.0 1250.0 1375.0 1500.0 1625.0 1750.0 1875.0 2000.0 2125.0 2250.0 2375.0 2500.0 2625.0 2750.0 2875.0 3000.0 3125.0 3250.0 3375.0 3500.0 3625.0 3750.0 3875.0 4000.0 4500.0 5000.0 5500.0 6000.0 6750.0 7500.0 ## Coverage area The forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area: * Southwest: 48.8643°N, 10.6734°W * Northwest: 61.3322°N, 13.7254°W * Northeast: 61.6102°N, 4.3408°E * Southeast: 49.0594°N, 2.4654°E ## Timesteps The following time steps are available: * every hour from 0 to 54 hours * every 3 hours from 57 to 120 hours ## Update frequency There are three lengths of model run, each with its own update frequency: * Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. * Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. * Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC. ## 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 3-6 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 UKV 2km deterministic 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.

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of June 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

GFS model output is processed into APCP rasters that capture accumulated precipitation over a 7‑day forecast period. These rasters are then combined with watershed boundary data, and zonal statistics are applied to compute average precipitation for each sub‑basin. The final polygon features provide a clear depiction of global rainfall and snowfall patterns, offering critical information for disaster risk management and international water resource planning.

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents the month of November within a dataset of 12 raster files (one for each month) representing total precipitation values across North America for the 1981–2010 climatological reference period, in millimeters. The dataset is derived from a kilometer-scale climate product generated using the CHELSA downscaling model and represents long-term climatological means. The North American monthly precipitation 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 precipitation 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 precipitation 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

saf_ann_full_ir_tropopause_difference_99th_percentile

EnviroAtlas image service of HistAnnualTempMax for time slider

ArcGIS Image ServiceTime Interval: Full Climatology 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 IR detection climatological diurnal statistics averaged over 2005-2025

EnviroAtlas image service of HistSummerPET for time slider

EnviroAtlas image service of HistSpringTempMin for time slider

EnviroAtlas image service of HistSpringTempMax for time slider

Raster counts of overshooting top days per month, presented with hourly timesteps, as part of a South Africa convection climatology. Published by mstisdal.

South Africa Climatology Hourly 2005-2019 IR OT Hour Count

TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in Zarr format.

ArcGIS Image ServiceTime Interval: Seasonal Climatology Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African OT detection climatological statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2005-2025

The High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model. HRRR runs are initialized by 3-km grids with 3-km radar assimilation. Radar data is assimilated in the HRRR every 15 minutes over a 1-hour period adding further detail to that provided by the hourly overarching 13-km Rapid Refresh (RAP) model. The HRRR model is especially useful for short-term weather forecasting, including severe weather events, aviation weather, and renewable energy applications. The data are available in GRIB2 format and are delivered to Azure as part of the NOAA Open Data Dissemination (NODD) Program.

Layer Overview and Intended UseThe Tropospheric Emissions: Monitoring of Pollution (TEMPO) Nitrogen Dioxide Vertical Column Troposphere Near Real-Time (NRT) layer provides information on the amount of nitrogen dioxide in the troposphere. This is provided as the total number of nitrogen dioxide molecules in the tropospheric column of air above one square centimeter on the Earth’s surface (units: molecules/cm^2). The TEMPO NRT nitrogen dioxide files are produced using a fast version of the TEMPO baseline retrieval processor. TEMPO NRT nitrogen dioxide Level 3 files provide trace gas information on a regular grid. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle, using an area-weighted re-gridding approach. The NRT processing optimizes radiance calibration, image navigation and registration, and cloud and trace gas retrievals for rapid processing, while ensuring that product quality is sufficient for most NRT applications. The data have been converted from their native file format (netCDF4) to Cloud Raster Format (CRF). Temporal CoverageThe temporal resolution of a nominal scan is approximately one hour during daylight hours, with more frequent scans in the morning over the eastern portion of the field of regard and in the evenings over the western portion of the field of regard. Each image is presented with the starting timestamp of the corresponding TEMPO scan. Due to the nature of the TEMPO instrument’s east to west scanning pattern, each image is a composite of measurements taken over a period of 40-60 minutes, depending on the spatial coverage of the scan. Data are updated hourly with any new scans that have been received. Data older than two days (48 hours) are removed from the service. On the rare occasion that the TEMPO instrument is not collecting data for an extended period of time, this service will retain only the latest scan until operations resume. The TEMPO operations log may be found on the TEMPO site.  Geographic CoverageImagery is available for the TEMPO field of regard covering the continental United States (CONUS), and much of Mexico, Canada, and the Caribbean. The imagery in this image service is regridded onto a 0.02º longitude x 0.02º latitude grid and presented in the geographic coordinate system WGS 1984. The sensor’s native spatial resolution is ~2 km x 4.75 km at the center of TEMPO’s field of regard (33.5°N, 89.2°W) in WGS 1984.   Data FilteringThe layer is filtered to remove low-quality pixels using the main data quality flag (removing low confidence measurements), solar zenith angle (removing data retrieved at high solar zenith angles), and effective cloud fraction (removing data where clouds obscure the tropospheric column) variables. The filters applied are set to remove pixels based on the following: main_data_quality_flag > 1, eff_cloud_fraction > 0.5, or solar_zenith_angle > 80.  Data Validation and Limitatations of UseThese data should be considered as provisional products per the Provisional Product Maturity level defined in the TEMPO validation plan. These data are at provisional maturity, which means that product performance has been demonstrated through a large, but still (seasonally or otherwise) limited number of independent measurements. The analysis is sufficient for limited qualitative determinations of product fitness-for-purpose, and the product is potentially ready for testing by operational users and may be suitable for scientific publication. Users may consult the TEMPO User Guide for descriptions of the data and associated known issues.  The information shown here is tropospheric column density (molecules per unit area in the lower atmosphere), not a concentration. Atmospheric concentration refers to the fractional amount of trace gas (e.g., nitrogen dioxide) relative to total air at a specific point, and changes with altitude. Conversion from column density to atmospheric concentration requires additional information, such as the state of the atmosphere and the vertical distribution of the column, which are not provided in this layer. The use of this layer to derive atmospheric concentrations of nitrogen dioxide is discouraged for the non-specialist. Recommended Usage NotesWhen viewing the image service in the Esri online map viewer, it is recommended to use the multidimensional slider rather than the default time slider. The multidimensional slider can be accessed via the “multidimensional” icon in the right-hand menu.  While qualitative analyses and general trends may remain similar between the TEMPO standard and NRT versions, merging versions for quantitative analysis is discouraged. For details on the changes in the data, please see the TEMPO User Guide and the TEMPO NRT Data Products User Guide and ATBD Supplement on the TEMPO site.  The TEMPO NRT ArcGIS service provides access to TEMPO NO2 NRT data earlier than the TEMPO standard NO2 data are available in the standard NO2 ArcGIS service. The TEMPO NRT products are useful for applications with low-latency requirements; however, it is recommended to switch to the TEMPO NO2 Standard Version 04 (V04) service, once the data becomes available in that service. TEMPO V04 data are available from September 17, 2025 to present and the service is updated daily with the previous day’s data. TEMPO Mission OverviewThe Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is a grating spectrometer, sensitive to visible (VIS) and ultraviolet (UV) wavelengths of light with a spectral range of 290-490 + 540-740 nm and 0.6 nm spectral resolution. The TEMPO instrument is attached to the Earth-facing side of a commercial telecommunications satellite (Intelsat 40e) in geostationary orbit over 89˚ W longitude (about 22,000 miles above Earth’s equator). This allows TEMPO to maintain a continuous view of North America so that the instrument's light-collecting mirror can make a complete east-to-west scan of the field of regard hourly during daylight hours. By measuring sunlight reflected and scattered from the Earth's surface and atmosphere back to the instrument's detectors, TEMPO's ultraviolet and visible light sensors provide measurements of ozone, nitrogen dioxide, formaldehyde, and other constituents involved in the chemical dynamics of Earth’s atmosphere. The primary mission objective of TEMPO is to improve our understanding of air quality and its impacts. By providing near real-time data and comprehensive atmospheric composition measurements, TEMPO helps advance knowledge of the Earth system and improve air quality forecasting. These data also aid in quantifying how air quality affects vegetation, agriculture, and human health. ContactFor inquiries about this service, please contact larc-dl-asdc-tempo@mail.nasa.gov or post/view questions on the Earthdata Forum.