Americas GIS data

Temporal coverage: Current calendar year - Today Data Sources: VIIRS Sensor on NOAA-20 Time Interval: Every 12 Hours Data Latency: ~ 9 - 12 hours from satellite overpass Projection: EPSG:4326 Spatial Extent: Contiguous United States, Alaska, and Canada. Portions of Alaska west of 169 Degrees west and portions of Canada north of 75 degrees north are excluded. Resolution: Vector data derived from the 375 m nadir resolution of the VIIRS sensors Purpose / use case: The FEDS algorithm is an “event-based” approach that tracks the growth and intensity of individual fires every 12 hours and treats them as discrete objects that persist, expand, and evolve together. This tracking strategy allows for detailed monitoring of large fires in space and time as they spread across a landscape. The algorithm clusters VIIRS active fire observations into specific fire events, and then estimates a fire perimeter polygon, the active segment of the perimeter, and metrics for each fire, such as the total estimated area (km^2) and length of the active fire front (km). Each 12-hour increment also includes information on a fire’s mean Fire Radiative Power (FRP), which is a metric that describes fire intensity as estimated by the satellite sensor. This dataset is best used for near real-time situational awareness and retrospective scientific analysis of fire behavior. Notes on Interpretation of the Data: As a satellite-based data product, FEDS has lower spatial resolution and accuracy than fire perimeters derived in operational contexts, such as those from airborne imagers. However, FEDS can provide information when other sources are unavailable, and the algorithm runs automatically in fixed 12-hr intervals for all clusters of fire activity greater than 4 km^2. The FEDS algorithm inherits the same uncertainties associated with the VIIRS active fire product. These include potential geolocation errors that can be relevant for interpreting fire locations and fire spread. For example, hot plumes of smoke can generate false positive detections that overestimate the size of the fire at that time step. In addition, active fire detections may be clustered into events that do not align directly with operational definitions of fire events (i.e., FEDS may group multiple agency-designated fires into a single fire event, or consider a single agency-designated fire to be multiple fire events). Finally, clouds or thick smoke may lead to the omission of VIIRS active fire detections, leading to an underestimate of fire activity and fire spread. More information on the interpretation of the VIIRS Active Fire Dataset for fires can be found in the NASA Fire Information for Resource Management System (FIRMS) FAQ. Source information/ Program Information: This dataset was developed as part of the NASA Earth Information Systems (EIS) project. This dataset was generated through the Wildfire Tracking Lab –a collaboration between scientists at NASA Goddard, NASA Langley, NASA Marshall, University of California Irvine, and University of Maryland. Citation: Chen et al. “California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.” Scientific data 9.1 (2022): 249 https://doi.org/10.1038/s41597-022-01343-0. Repository: The underlying algorithm that generates these data layers is hosted and developed at the Fire Events Data Suite repository. Parameters: Column Name: t Description: Time of VIIRS detection. Data collected during approximately 1:30 AM and 1:30 PM local solar time overpasses. The time component of the datetime is assigned a local time of 00:00:00 for 1:30 AM overpasses, and 12:00:00 for 1:30 PM overpasses. Unit is Datetime. yyyy-mm-ddThh:mm:ss. Time component is in local solar time. Date component is the UTC date. UTC date will match local date for all of CONUS and Canada, but will not match for Alaska. Column Name: region Description: Descriptive name of larger region where tracking is occurring. Column Name: fireid Description: Fire ID. Unique for each fire within a given region. Unit is Numeric ID Column: geometry Description: The location of the pixels. Unit is Geometry. CRS is EPSG:4326. Column: Primary Key Description: Globally unique identifier for a given layer entry. This key is formatted by [region]|[fireid]|[timestamp]. Unitless. Alternate Forms Of Data Access: This data can also be accessed through an API. Documentation for the API can be found here. The API distributes data in open source data formats. Contact: For inquiries about service performance please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum. For questions about the data, please contact the science team with this form.

South America Seasonal Full 1995 2021 IR Anvil Day Count

sam_seas_full_1995_2021_cloud_top_height_99th_percentile

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

South America Monthly Cloud Top Height 99th Percentile

sam_annual_hr_ir_tropopause_difference_99th_percentile

Overview This layer is an image of nighttime lights of North America, including the Caribbean and Mexico, produced through a collaborative effort between the national atlas programs of Canada, Mexico, and the United States, in partnership with the Commission for Environmental Cooperation (CEC). The data were collected in 1996 and 1997 as part of the Defense Meteorological Satellite Program (DMSP) of the United States. DMSP satellites collect visible and infrared cloud imagery, as well as monitoring atmospheric, oceanographic, hydrologic, cryospheric, and near-Earth space environments. The program maintains a constellation of two sun-synchronous, near-polar orbiting satellites, with an orbital period of 101 minutes and an inclination of 99 degrees. This image was created during the periods 16–21 March 1996, 5–14 January 1997, and 3–12 February 1997, when the gain on the OLS sensor was turned down to 24 from the normal 64 decibels so that urban centers did not saturate the signal.  This is a revised version made in May 2002 map layer, with a corrected shoreline for Greenland. Geographic Coverage The coverage spans the North American continent, including Canada, Mexico, and the United States, as well as the Caribbean. Data Sources and Licensing Commission for Environmental Cooperation (CEC), based on data from the National Oceanic and Atmospheric Administration (NOAA) - National Geophysical Data Center (NGDC). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation, including for commercial purposes, with appropriate attribution. Update frequency: As needed Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and/or the National Oceanic and Atmospheric Administration (NOAA) - National Geophysical Data Center (NGDC) would be appreciated in products derived from these data. Vintage: 1996–1997 Applications (Intended Use) Visualizing urbanization and human settlement patterns across North America. Research on population distribution and land use as inferred from artificial lighting. Basemap and reference layer for continental-scale environmental analysis. Integration with other thematic layers in the North American Environmental Atlas for comprehensive continental analysis. Files Download

Temporal coverage: Current calendar year - Today Data Sources: VIIRS Sensor on NOAA-20 Time Interval: Every 12 Hours Data Latency: ~ 9 - 12 hours from satellite overpass Projection: EPSG:4326 Spatial Extent: Contiguous United States, Alaska, and Canada. Portions of Alaska west of 169 Degrees west and portions of Canada north of 75 degrees north are excluded. Resolution: Vector data derived from the 375 m nadir resolution of the VIIRS sensors Purpose / use case: The FEDS algorithm is an “event-based” approach that tracks the growth and intensity of individual fires every 12 hours and treats them as discrete objects that persist, expand, and evolve together. This tracking strategy allows for detailed monitoring of large fires in space and time as they spread across a landscape. The algorithm clusters VIIRS active fire observations into specific fire events, and then estimates a fire perimeter polygon, the active segment of the perimeter, and metrics for each fire, such as the total estimated area (km^2) and length of the active fire front (km). Each 12-hour increment also includes information on a fire’s mean Fire Radiative Power (FRP), which is a metric that describes fire intensity as estimated by the satellite sensor. This dataset is best used for near real-time situational awareness and retrospective scientific analysis of fire behavior. Notes on Interpretation of the Data: As a satellite-based data product, FEDS has lower spatial resolution and accuracy than fire perimeters derived in operational contexts, such as those from airborne imagers. However, FEDS can provide information when other sources are unavailable, and the algorithm runs automatically in fixed 12-hr intervals for all clusters of fire activity greater than 4 km^2. The FEDS algorithm inherits the same uncertainties associated with the VIIRS active fire product. These include potential geolocation errors that can be relevant for interpreting fire locations and fire spread. For example, hot plumes of smoke can generate false positive detections that overestimate the size of the fire at that time step. In addition, active fire detections may be clustered into events that do not align directly with operational definitions of fire events (i.e., FEDS may group multiple agency-designated fires into a single fire event, or consider a single agency-designated fire to be multiple fire events). Finally, clouds or thick smoke may lead to the omission of VIIRS active fire detections, leading to an underestimate of fire activity and fire spread. More information on the interpretation of the VIIRS Active Fire Dataset for fires can be found in the NASA Fire Information for Resource Management System (FIRMS) FAQ. Source information/ Program Information: This dataset was developed as part of the NASA Earth Information Systems (EIS) project. This dataset was generated through the Wildfire Tracking Lab –a collaboration between scientists at NASA Goddard, NASA Langley, NASA Marshall, University of California Irvine, and University of Maryland. Citation: Chen et al. “California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.” Scientific data 9.1 (2022): 249 https://doi.org/10.1038/s41597-022-01343-0. Repository: The underlying algorithm that generates these data layers is hosted and developed at the Fire Events Data Suite repository. Parameters: Column Name: t Description: Time of VIIRS detection. Data collected during approximately 1:30 AM and 1:30 PM local solar time overpasses. The time component of the datetime is assigned a local time of 00:00:00 for 1:30 AM overpasses, and 12:00:00 for 1:30 PM overpasses. Unit is Datetime. yyyy-mm-ddThh:mm:ss. Time component is in local solar time. Date component is the UTC date. UTC date will match local date for all of CONUS and Canada, but will not match for Alaska. Column Name: region Description: Descriptive name of larger region where tracking is occurring. Column Name: fireid Description: Fire ID. Unique for each fire within a given region. Unit is Numeric ID Column Name: pixden Description: Number of total pixels (n_pixels) divided by area of fire perimeter. Unit is pixels/Km^2 Column Name: duration Description: Number of days since first observation of fire. Fires with a single observation have a duration of zero. Unit is Days. Column Name: flinelen Description: Length of active fire segment, based on new pixels. If no new pixels are detected, flinelen is set to zero. Unit is Km. Column: fperim Description: Length of fire perimeter. Unit is Km. Column: farea Description: Area within fire perimeter. Unit is Km^2 Column Name: meanfrp Description: Mean fire radiative power for detected active fire pixels. The sum of the fire radiative power divided by the number of newly detected pixels at each time step. If no new pixels are detected, meanfrp is set to zero for that timestep. Units are MW/pixel. Column: n_newpixels Description: Number of pixels newly detected since last overpass. Unit is pixels. Column: n_pixels Description: Number of total pixel-detections in history of fire. Unit is pixels. Column: isactive Description: Boolean indicator of recent fire activity. If no new detections occur within N days, this is marked as “False” The number of days is defined regionally, and has a default of 5. See repository information. Unit is Boolean. Column: geometry Description: The shape of the perimeter. Unit is Geometry. Can be a Polygon or Multipolygon. CRS is EPSG:4326. Column: Primary Key Description: Globally unique identifier for a given layer entry. This key is formatted by [region]|[fireid]|[timestamp]. Unitless. Alternate Forms Of Data Access: This data can also be accessed through an API. Documentation for the API can be found here. The API distributes data in open source data formats. Contact: For inquiries about service performance please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum. For questions about the data, please contact the science team with this form.

Overview This map layer presents a dataset of named populated places across North America, encompassing Canada, the United States, and Mexico, suitable for use at a continental scale of 1:10,000,000. The information is derived from the national mapping and census agencies of each country: Natural Resources Canada (NRCan), the Instituto Nacional de Estadística y Geografía (INEGI), and the U.S. Geological Survey (USGS). It is a revised version of the 2004 data set, released as part of a joint venture coordinated by the Commission for Environmental Cooperation (CEC). Places, which refer to individual municipalities, are represented as points fitted to the North American Environmental Atlas roads, railroads, and hydrography layers, so that each point reflects the approximate location of a place relative to those features. The selection of populated places was based on local importance as indicated by population size, importance as a cross-border point, and occasionally other factors. All capital cities (national, provincial, territorial, or state) are included for Canada, Mexico, and the United States. The dataset supports multiple languages for attribute names, including English, Spanish, and French, facilitating accessibility across North America. It also includes standardized identifiers and classification codes for population class, capital status, country, and state or province for each place. Geographic Coverage The coverage spans North America, including the continental territories of Canada, the United States, and Mexico. Features located outside these three countries are included only to provide geographic context and carry no completeness or accuracy guarantees from the contributing agencies. Data Sources and Licensing This data is produced by the Commission for Environmental Cooperation (CEC), derived from national atlas and census programs in Canada (Natural Resources Canada, Statistics Canada), Mexico (INEGI), and the United States (U.S. Geological Survey, U.S. Census Bureau). The compiled dataset 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: Intended for continental-scale reference. Positional accuracy is dependent on the source data and integration processes. Data outside Canada, Mexico, and the United States is provided for context only; the CEC and partner agencies assume no responsibility for its completeness or accuracy. Vintage: The information presented in the map layer represents data compiled in 2009, based on national census records from 2000–2001. Applications (Intended Use) Continental-scale reference and base mapping Transportation and cross-border connectivity analysis Population distribution and urban settlement research Environmental and policy research requiring geographic context Educational and reference purposes Files Download

A raster climatology of annual overshooting top day counts per month for South America, derived from infrared satellite observations. Cell values indicate the number of days with overshooting tops. Published by mstisdal.

sam_ann_full_ir_tropopause_difference_99th_percentile

Raster climatology of annual anvil cloud day counts per month over South America, derived from full-disk infrared imagery. Published by mstisdal.

Monthly counts of overshooting top days per month from infrared observations, part of a South America convection climatology. Covers South America and the Caribbean, including Aruba, Barbados, Grenada, Martinique, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago, Costa Rica, El Salvador and Nicaragua. Published by mstisdal.

South America Seasonal Hourly 1995-2021 IR OT Hour Count

Raster counts of anvil cloud days per month with hourly timesteps, as part of a South America convection climatology. Published by mstisdal.

Address points linked to plat tracker boundary applications, with each feature carrying an application number, review cycle, application code and status, subdivision name and section, lot and reserve counts, reserve size, and upload and plat check dates. Published by cohgis_ago.

South America CLimatology Full 1995 2021 IR Tropopause Difference 99th Percentile

This raster dataset provides a full climatology of anvil cloud days per month for South America, based on data from 1995 to 2021. It covers multiple countries including Aruba, Barbados, Grenada, Martinique, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago, Costa Rica, El Salvador, and Nicaragua. Published by mstisdal.

Annual climatology of hourly overshooting top counts per month for South America convection, derived from infrared imagery. Published by mstisdal with the Langley Research Center.

Date of Images: Post-Event: May 3, 2022-May 12, 2022 (Daily) Pre-Event: April 2022 (Composite) 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. 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. 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: 15 arc-second (~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. Service URL: See REST Endpoint on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_blackmarble_brdf_cloud/ImageServer/WMSServer

Date of Images: Post-Event: May 3, 2022-May 12, 2022 (Daily) Pre-Event: April 2022 (Composite) 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. 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. 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: 15 arc-second (~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. Service URL: See REST Endpoint on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_blackmarble_brdf/ImageServer/WMSServer

sam_seas_full_ir_tropopause_difference_99th_percentile

Overview This layer shows North America classified into "anthropogenic biomes," also known as "anthromes" or "human biomes." Biomes are major global ecosystem types, characterized by particular climate and soil conditions and distinctive forms of life adapted to these conditions. Incorporating the significant role of humans in altering ecosystems, this layer offers a new way of classifying the parts of the earth in which life exists.  Geographic Coverage The coverage spans the entire North American continent, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC), based on the anthropogenic biomes classification developed by Erle C. Ellis and Navin Ramankutty in 2008, Putting people in the map: Anthropogenic biomes of the world. Frontiers in Ecology and the Environment 6, doi:10.1890/070062. The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation, including for commercial purposes, with appropriate attribution. Update frequency: Not planned Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and/or Ellis & Ramankutty (2008) would be appreciated in products derived from these data. Vintage: 2008 Applications (Intended Use) Research on human influence on terrestrial ecosystems and biodiversity. Land use and land cover analysis integrating human population patterns. Conservation planning that accounts for the degree of human alteration of natural systems. Integration with other thematic layers in the North American Environmental Atlas for comprehensive continental analysis. Files Download

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

sam_clim_hr_ir_tropopause_difference_99th_percentile

South America Climatology Hourly IR Anvil Hour Count

sam_seas_hr_1995_2021_cloud_top_height_99th_percentile

Overview This map layer prepared by the Commission for Environmental Cooperation (CEC) shows the direct influence of humans on terrestrial ecosystems across North America. The Human Influence Index (HII) is based on population density, built-up areas, roads, railroads, navigable rivers, coastlines, land use/land cover, and nighttime lights. HII values range from 0 to 64, with 0 representing no human influence and 64 representing maximum human influence, based on all eight measures of human influence. The data layer was compiled by the Wildlife Conservation Society (WCS) and the Center for International Earth Science Information Network (CIESIN).  Geographic Coverage The coverage spans terrestrial areas of North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). The Last of the Wild, Version Two. 2005. Global human influence index (HII). Wildlife Conservation Society (WCS), and Center for International Earth Science Information Network (CIESIN). 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: Not planned Limitations of use: Acknowledgment of the Wildlife Conservation Society (WCS) and the Center for International Earth Science Information Network (CIESIN) is appreciated in products derived from these data. Vintage: 2005 Applications (Intended Use) Conservation planning and identification of low-impact/wilderness areas. Assessing human pressure on biomes and ecoregions across North America. Supporting natural resource management and sustainability research. Cross-border environmental cooperation and policy-making among North American nations. Files Download

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.

Temporal coverage: Current calendar year - Today Data Sources: VIIRS Sensor on NOAA-20 Time Interval: Every 12 Hours Data Latency: ~ 9 - 12 hours from satellite overpass Projection: EPSG:4326 Spatial Extent: Contiguous United States, Alaska, and Canada. Portions of Alaska west of 169 Degrees west and portions of Canada north of 75 degrees north are excluded. Resolution: Vector data derived from the 375 m nadir resolution of the VIIRS sensors Purpose / use case: The FEDS algorithm is an “event-based” approach that tracks the growth and intensity of individual fires every 12 hours and treats them as discrete objects that persist, expand, and evolve together. This tracking strategy allows for detailed monitoring of large fires in space and time as they spread across a landscape. The algorithm clusters VIIRS active fire observations into specific fire events, and then estimates a fire perimeter polygon, the active segment of the perimeter, and metrics for each fire, such as the total estimated area (km^2) and length of the active fire front (km). Each 12-hour increment also includes information on a fire’s mean Fire Radiative Power (FRP), which is a metric that describes fire intensity as estimated by the satellite sensor. This dataset is best used for near real-time situational awareness and retrospective scientific analysis of fire behavior. Notes on Interpretation of the Data: As a satellite-based data product, FEDS has lower spatial resolution and accuracy than fire perimeters derived in operational contexts, such as those from airborne imagers. However, FEDS can provide information when other sources are unavailable, and the algorithm runs automatically in fixed 12-hr intervals for all clusters of fire activity greater than 4 km^2. The FEDS algorithm inherits the same uncertainties associated with the VIIRS active fire product. These include potential geolocation errors that can be relevant for interpreting fire locations and fire spread. For example, hot plumes of smoke can generate false positive detections that overestimate the size of the fire at that time step. In addition, active fire detections may be clustered into events that do not align directly with operational definitions of fire events (i.e., FEDS may group multiple agency-designated fires into a single fire event, or consider a single agency-designated fire to be multiple fire events). Finally, clouds or thick smoke may lead to the omission of VIIRS active fire detections, leading to an underestimate of fire activity and fire spread. More information on the interpretation of the VIIRS Active Fire Dataset for fires can be found in the NASA Fire Information for Resource Management System (FIRMS) FAQ. Source information/ Program Information: This dataset was developed as part of the NASA Earth Information Systems (EIS) project. This dataset was generated through the Wildfire Tracking Lab –a collaboration between scientists at NASA Goddard, NASA Langley, NASA Marshall, University of California Irvine, and University of Maryland. Citation: Chen et al. “California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.” Scientific data 9.1 (2022): 249 https://doi.org/10.1038/s41597-022-01343-0. Repository: The underlying algorithm that generates these data layers is hosted and developed at the Fire Events Data Suite repository. Parameters: Column Name: t Description: Time of VIIRS detection. Data collected during approximately 1:30 AM and 1:30 PM local solar time overpasses. The time component of the datetime is assigned a local time of 00:00:00 for 1:30 AM overpasses, and 12:00:00 for 1:30 PM overpasses. Unit is Datetime. yyyy-mm-ddThh:mm:ss. Time component is in local solar time. Date component is the UTC date. UTC date will match local date for all of CONUS and Canada, but will not match for Alaska. Column Name: region Description: Descriptive name of larger region where tracking is occurring. Column Name: fireid Description: Fire ID. Unique for each fire within a given region. Unit is Numeric ID Column: geometry Description: The shape of the Fire Segment. Unit is Geometry. CRS is EPSG:4326. Column: Primary Key Description: Globally unique identifier for a given layer entry. This key is formatted by [region]|[fireid]|[timestamp]. Unitless. Alternate Forms Of Data Access: This data can also be accessed through an API. Documentation for the API can be found here. The API distributes data in open source data formats. Contact: For inquiries about service performance please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum. For questions about the data, please contact the science team with this form.

A raster climatology of the 99th percentile cloud top height over South America, derived from satellite observations. Cell values give the 99th percentile cloud top height. Published by mstisdal.

South America Seasonal Full 1995-2021 IR OT Day Count

Overview This layer shows the locations of more than 3,000 North American power-generating facilities using fossil fuel sources in 2005, produced through a collaborative effort between the national atlas programs of Canada, Mexico, and the United States, in partnership with the Commission for Environmental Cooperation (CEC).  Each facility includes information on its primary fuel, generating capacity, and air emissions of SO2, NOx, Hg, PM2.5, PM10, CH4, N2O, and CO2. Facilities are classified based on the primary source of fuel used to generate electricity: oil, natural gas, coal, or other fuels. This map layer was developed for the CEC's 2011 publication, North American Power Plant Air Emissions. Fossil fuels are the main energy source used for electricity generation in North America, producing almost two-thirds of the electricity consumed. The data were assembled primarily from the three North American countries' national emission inventories: Canada's National Pollutant Release Inventory (NPRI) and Greenhouse Gas Emissions Reporting Program (GHGRP); the United States' Emissions and Generation Resource Integrated Database (eGRID2007) and National Emissions Inventory (NEI); and Mexico's Inventario Nacional de Emisiones (INEM), compiled from publicly available databases and reports. A Google Earth (KMZ) map of the 3,144 power plants and their air emissions is also available. For more information. Geographic Coverage The coverage spans the entire North American continent, including power-generating facilities located in Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC), based on data provided by multiple agencies from each country, including Environment and Climate Change Canada (ECCC), Statistics Canada, the U.S. Environmental Protection Agency (EPA), and Mexico's Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT), Secretaría de Energía (SENER), Comisión Federal de Electricidad (CFE), and Comisión Reguladora de Energía (CRE). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation, including for commercial purposes, with appropriate attribution. Update frequency: Unknown Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation would be appreciated in products derived from these data. Vintage: 2005 Applications (Intended Use) Air quality assessment and emissions tracking for the electric power sector. Environmental policy analysis and cross-border comparison of power plant emissions. Research on the relationship between fuel type, generating technology, and pollutant emissions. Integration with other thematic layers in the North American Environmental Atlas for comprehensive continental analysis. Files Download

sam_monthly_ir_tropopause_difference_99th_percentile

1. Hurricane Irma's gust footprint for 3-sec maximum gusts through to 9UT 7th September 2017. 2. Hurricane Irma's forecast gust swathe out to 120hrs lead for 3-sec maximum gusts (issued 9UT 7th September). 3. Hurricane Irma peak 3-sec gust footprints in kml format (through 15UT 11th Sept 2017) Credit - Tropical Storm Risk/UCL

# Grenada St Vincent and the Grenadines Hurricane Beryl: Damage Proxy Maps, Jul 2024 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Grenada St Vincent and the Grenadines Hurricane Beryl (Jul 2024) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 wbd** (superseded) - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 wbd** (superseded) - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 ** - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 ** **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024)