CLDN

Real-time data on lightning detected by DMI's lightning goggle network in Denmark as well as a number of third-party lightning goggles in surrounding countries. The lightning sticks detect both lightning going from cloud to earth and lightning from cloud to cloud.

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.

Monthly raster climatology of lightning flash rate density from the TRMM Lightning Imaging Sensor, produced by NASA Langley Research Center as part of the SMASH project.

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.

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.

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