Wildfires Data Pathfinder - Find Data

This NASA data pathfinder links to NASA datasets and tools that can aid with decisions regarding land fire management before, during, and after an event.

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides data to the public within three hours of satellite observation, which allows for near real-time (NRT) monitoring and decision making. Specifically for fires, both the MODIS instrument aboard the Terra and Aqua satellites and the VIIRS instrument aboard the joint NOAA/NASA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite and the NOAA-20 satellite provide fire information on hotspots/fires and thermal anomalies, and smoke plume movement via true color reflectance imagery.

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Terra MODIS Corrected Reflectance imagery of Camp Fire, 2018, through Worldview.

Suspended particles in the air that are made up of smoke and dust from burning fires can be tracked and measured by instruments aboard NASA Earth observing satellites. Using MODIS or VIIRS imagery, you can follow a smoke plume and its movement through time. NASA's Worldview application provides the capability to interactively browse over 1000 global, full-resolution satellite imagery layers and download the underlying data. Many of the imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now."

For a tutorial see Getting Started with NASA Worldview. Worldview has updated the Satellite Detections of Fire Tour Story that highlights products like vector layers, the PyroCumuloNimbus (pyroCb) Aerosol Index, the Blue/Yellow Composite Day/Night Band, and the GOES-West GeoColor animation of wildfires.

Surface reflectance data from the MODIS and VIIRS instruments provides a means to measure the spatial distribution and intensity of smoke plumes. A high resolution option is the new (but currently PROVISIONAL) Harmonized Landsat and Sentinel-2 (HLS) project, which provides consistent surface reflectance and top of atmosphere brightness data from the OLI aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard the European Space Agency's Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2–3 days at 30 m spatial resolution.

In addition, the red visible imagery layer of the geostationary satellites, primarily used to monitor the evolution of clouds, can also be used to identify smoke plume movement. The GOES-East satellite is centered on 75.2 degrees W, covering the Conterminous US, Canada, Central and South America (so most of the Atlantic Ocean). GOES-West is centered on 137.2 degrees W, covering most of the Pacific Ocean, the U.S., most of Canada, Central, the western half of South America, and parts of Australasia. The Himawari-8 satellite is centered on 140.7 degrees E, covering most of the Pacific Ocean, a portion of Eastern Asia, and parts of Australasia. Data are acquired every 10 minutes and are available on a rolling 30-day window. Note: The Red Visible imagery is only viewable during the day time. Change the time in the timeline if the imagery is black over your area of interest.

  • MODIS Corrected Reflectance in Worldview
    The MODIS Corrected Reflectance imagery is available only as near real-time imagery. The MODIS Corrected Reflectance algorithm utilizes MODIS Level 1B data (the calibrated, geolocated radiances). It is not a standard, research quality product. The purpose of this algorithm is to provide natural-looking images by removing gross atmospheric effects, such as Rayleigh scattering, from MODIS visible bands 1-7.
  • VIIRS Corrected Reflectance in Worldview
    The VIIRS Corrected Reflectance imagery is available only as near real-time imagery.
  • HLS Surface Reflectance in Worldview
Geostationary Imagery

Aerosol Index (AI)

Aerosols absorb and scatter incoming sunlight, which reduces visibility and increases the optical depth. Satellite-derived AI products are useful for identifying and tracking the long-range transport of smoke from wildfires or biomass burning events. Currently there are two near real-time Aerosol Index data products available, one from Aura OMI and the other from the Suomi NPP OMPS. AI indicates the presence of ultraviolet (UV)-absorbing particles in the air (aerosols); the higher the AI, the higher the concentration in the atmosphere. For both satellites, the spatial resolution is 2 km and the temporal resolution is daily.

  • OMPS AI in Worldview
    AI from OMPS includes a newer product, PyroCumuloNimbus (pyroCb), which makes it easier to track the extent and spread of pyroCb and other high-aerosol events. Typically the AI signal remains below 5.0 for most smoke and dust events, the OMPS AI product with an AI range of 0.0 to 5.0 satisfies the needs of most users. However, the AI signal for pyroCb events, which are both dense and high in the atmosphere, easily can be much larger than 5.0. In fact, the highest AI value ever observed (55.0) occurred during a pyroCb event in Canada, August 2017.
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Locations of burning fires (top image) compared to average monthly aerosol optical depth (bottom image). Credit: NASA's Earth Observatory.

Aerosol Optical Depth (AOD)

AOD indicates the level at which particles in the air (aerosols) prevent light from traveling through the atmosphere. From an observer on the ground, an AOD of less than 0.1 is "clean" - characteristic of clear blue sky, bright sun and maximum visibility. As AOD increases to 0.5, 1.0, and greater than 3.0, aerosols become so dense that the sun is obscured.

  • MODIS AOD in Worldview
    Aqua and Terra's MODIS Combined Value-Added Aerosol Optical Depth layer is a value-added layer based on MODIS Level 2 aerosol products. The layer can give a quick, synoptic view of the level of aerosols in the atmosphere.
  • MODIS Deep Blue AOD in Worldview
    Deep Blue AOD layer is useful for studying aerosol optical depth over land surfaces. This layer is created from the Deep Blue algorithm.
  • AERONET
    Ground-based AOD measurements are available online at the Aerosol Robotic Network (AERONET).

Trace Gases from Fires

In addition to the particulate matter, numerous trace gases are found in the atmosphere during and after a fire event. These trace gases, like carbon monoxide (CO) and sulfur dioxide (SO2), are harmful pollutants that can impact public health. Data for trace gases are available from a variety of different satellites. For CO, the Atmospheric Infrared Sounder (AIRS) onboard the Aqua satellite provides the best global coverage at 2 km resolution and twice daily measurements (day and night). AIRS also provides measurements of SO2. Two other instruments, OMI and OMPS (descriptions above) provide information on SO2 at the lower troposphere, middle troposphere, and upper troposphere/stratosphere layers.

  • AIRS L2 CO (Day/Night) in Worldview
    CO in units of parts per billion by volume at the 500 hPa pressure level, approximately 5500 meters (18,000 feet) above sea level. AIRS Level 2 data are nominally 45 km/pixel at the equator and the data has been resampled into a 32 km/pixel visualization.
  • AIRS SO2 in Worldview
    Indicates SO2 column amounts in the atmosphere, measured in Dobson Units (DU).
  • OMI/OMPS SO2 in Worldview
    Indicates the column density of SO2 at different layers of the atmosphere and is measured in Dobson Units (DU). OMI data are available from 2005-present and OMPS from 2012-present.
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Ground observations versus output display for MODIS sensor.

There are some differences between the fire datasets that must be considered. Each of these instruments has a different spatial and temporal resolution. MODIS data are at 1km and are acquired daily (Terra satellite passes over in the morning and Aqua in the afternoon), whereas VIIRS are at 375m and are acquired daily, with improved nighttime performance over MODIS. The NOAA-20 satellite follows the same orbit as Suomi NPP, but lags behind by about 50 minutes.\

The thermal anomalies/fire NRT data are basically a snapshot in time, showing what is occurring at the moment the data were acquired. It is determined by a contextual algorithm that utilizes the infrared or thermal radiation of the fires. Each MODIS active fire represents the center of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel (see figure to right). As VIIRS has a higher resolution, it can pick up fires that MODIS overlooks, especially those covering relatively small areas.

It is important to note that the NRT products are not considered research quality because predicted geolocation is used. Research quality data, which are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science, are available with an approximate two to three months lag.

There are two primary ways of exploring NRT fire data through the Fire Information for Resource Management System (FIRMS): through an interactive map or by direct download of the NRT data. The interactive map provides NRT and the full archive of global MODIS and VIIRS fire locations. It also enables users to view the MODIS Terra/Aqua Global Burned Area data product (with an approximate 4-month lag between the date of burn and burned area data product in FIRMS).

LANCE FIRMS developers partnered with the USDA Forest Service’s Geospatial Technology and Applications Center (GTAC) to create FIRMS US/Canada, a new and expanded version of FIRMS. In addition to the standard FIRMS, FIRMS US/Canada meets the new Forest Service requirements by offering additional contextual layers and enhancements, including classifying fires to show time since detection to depict active fire fronts, incident locations and other information for current large fires in the US and Canada. FIRMS US/Canada provides current and historical corrected reflectance imagery from NASA and NOAA satellites, US and Canada administrative ownership boundaries, daily fire danger forecasts, and current National Weather Service fire weather watch and red flag warning areas.

Active fire data are available for download for any area of interest, in NRT and from the full archive. Within Worldview, fire and thermal anomalies are provided as vector layers, which have attribute information that can be examined when a vector feature is clicked. For example, when a point is clicked on in the layer, a table of attributes will appear including latitude, longitude, brightness temperature, and fire radiative power.

Research quality data products can be accessed via Earthdata Search.

For a tutorial on using FIRMS data, see the webinar Discover FIRMS on NASA's Earthdata YouTube channel. For more in depth information about FIRMS read the FIRMS Frequently Asked Questions.

NASA's Disasters Wildfire Products and Events

Wildland fire research and applications spans across multiple NASA programs, and fire itself, is an integral natural process that acts to maintain ecosystem biodiversity and structure. NASA Disasters creates products for specific events and provides other information to help interpret the data.

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Animation of the number of lightning flashes from the International Space Station (ISS) Lightning Imaging Sensor (LIS). Animation, from the Worldview Application, includes data from August 16-20, 2020; these lightning strikes began fires that are becoming some of the largest California fires in history.

The International Space Station (ISS) Lightning Imaging Sensor (LIS) is a space-based lightning sensor aboard the ISS. The ISS LIS instrument records the time of occurrence of a lightning event, measures the radiant energy and estimates the location during both day and night conditions with high detection efficiency.

 

Many factors contribute to a fire, its intensity and its severity, including changing weather patterns, land cover, vegetative health, drought conditions, and changing land use or land management. As such, it's important to monitor contributing factors in order to predict the formation of a fire and how it will move through the environment. NASA has several datasets for making these predictions.

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False-color image of Normalized Difference Vegetation Index (NDVI) data of King Fire area, September 2013 (left) and Nov 2014. Credit: ORNL DAAC

Vegetation indices have been developed to measure the amount of green vegetation over a given area and can be used to assess vegetation health. One commonly used vegetation index is the Normalized Difference Vegetation Index (NDVI), which takes the difference between NIR and red reflectance divided by their sum. NDVI values range from -1 to 1. Low values of NDVI generally correspond to barren areas of rock, sand, exposed soils, or snow. Increasing NDVI values indicate greener vegetation, including things like forests, croplands, and wetlands. Aqua and Terra's MODIS and Suomi NPP's VIIRS vegetation data products can be accessed via the following ways:

Research quality data products can be accessed directly via Earthdata Search; datasets are available in HDF format, which can be opened in Panoply.

Video file
Eleven years of MODIS Fires in Africa over 16 day composite MODIS NDVI and 16 day composite MODIS snow and ice. Credit: NASA's Scientific Visualization Studio.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NRT imagery can be accessed in Worldview.

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Near real-time IMERG Early Run Half-Hourly Image, acquired on May 7, 2020. Credit: NASA.

NASA's Precipitation Measurement Missions (PMM) provide a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

The products from these missions are available individually or have been integrated with a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing a temporal resolution of 30 minutes. TRMM has been integrated into the TRMM Multi-satellite Precipitation Algorithm (TMPA) and GPM into the Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG's multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours for flash flood events, Late = 12 hours for crop forecasting, and Final = 3 months for research with the incorporation of rain gauge data).

Research quality data products can be accessed via Earthdata Search:

  • IMERG from Earthdata Search
    Early, Late and Final precipitation data on the half hour or one day timeframe. Data are in NetCDF or HDF format, and can be opened using Panoply. Data are available from 2000.
  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day or NRT and accumulated rainfall at three hours and one day. Data are in HDF format, and can be opened using Panoply. Data are available from 1997.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NASA also has models of precipitation, incorporating satellite information with ground-based data when available. These models are part of the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates. In addition to IMERG and LDAS, the AMSR2 instrument collects data that indicates the rate at which precipitation is falling on the surface of the ocean.

Near real-time data can be accessed from Worldview:

Daymet is a collection of gridded estimates of daily weather parameters. It is modeled on daily meteorological observations. Weather parameters in Daymet include daily minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii.

Daymet data can be retrieved in a variety of ways, including: Earthdata Search; an Oak Ridge National Laboratory DAAC (ORNL DAAC) API; ORNL DAAC tools; and through the Land Processes DAAC (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS).

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Satellite images show the differences in land surface temperature during the day (middle image) and at night (bottom image). Top image is a natural color image. Darker colors indicate cooler temperatures. Heavily forested areas remain relatively cool throughout the day while barren and arid areas can be significantly warmer. These images were acquired over the state of Oregon, USA, in the early morning and afternoon of July 6, 2011. Image: NASA Earth Observatory.

Land surface temperature is useful for monitoring changes in weather and climate patterns and used in wildfire risk assessment.

Research quality land surface temperature data products can be accessed directly from Earthdata Search or LP DAAC's Data Pool; MODIS and ASTER data are available as HDF and VIIRS and ECOSTRESS are available as HDF5:

To quickly extract a subset of ECOSTRESS, MODIS, or VIIRS data for your region of interest, use LP DAAC's AppEEARSor ORNL DAAC'ssubsetting tools.

Landsat data can be discovered using Earthdata Search, however, you will need a USGS Earth Explorer login to download the data.

Data can be visualized in Worldview:

Soil moisture is important in forecasting fire events as the dryness of the soil contributes to the overall fire potential in the area. Satellite data can provide a synoptic view of soil moisture across the globe. Although ground- based measurements are at a higher resolution, the data are often sparse and have limited coverage. The preferred measurement should be chosen based upon your needs.

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SMAP's rotating golden antenna functions like a satellite dish to focus radio waves from Earth's surface into a collector on the spacecraft. Image: NASA JPL/Caltech

The SMAP satellite measures the moisture in the top 5 cm of the soil globally every three days, at a resolution of 10-40 km.

Research quality data products can be accessed via Earthdata Search; MODIS and VIIRS datasets are available as HDF files and can be opened using Panoply, but are also customizable to GeoTIFF:

The ORNL DAAC Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. See the Tools for Data Access and Visualization section for additional information. AppEEARS offers another option to simply and efficiently extract subsets, transform, and visualize SMAP data products.

NASA has models of soil moisture content, incorporating satellite information with ground-based data when available. These models are part of the LDAS. Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

In addition to SMAP, the AMSR2 instrument on the GCOM-W1 provides a NRT product, which is a daily measurement of surface soil moisture. NRT imagery can be accessed via Worldview:

Knowing the topography of an area is important so that fire managers and emergency management professionals can anticipate areas of risk, including direction and speed of wind, landslide potential and runoff.

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An ASTER GDEM image of Mt. Raung and the surrounding area. Image Credit: Land Processes Distributed Active Archive Center

A method for delineating topography is the Shuttle Radar Topography Mission (SRTM). SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The ASTER Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane.

On average, compared to geodetic points over the U.S., SRTM data has a lower root mean square error (RMSE); RMSE is a commonly used method to express vertical accuracy of elevation datasets. Digital elevation model data accuracy is typically very sensitive to vegetation cover. ASTER tends to perform better over certain landcover types.

February 2020, LP DAAC released a new data product, NASADEM, available at 1 arc-second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products. The improvements were achieved by reprocessing the original SRTM radar signal data and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides surface wind data beginning in 1980 and runs a few weeks behind real time.

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NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System, Version 5 (GEOS-5) Wind Speed Weather Map of North America during the 2020 Lightning Complex fires in California. Credit: NASA

MERRA-2 data products can be accessed via Earthdata Search:

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NASA's Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System, Version 5 (GEOS-5) has a series of weather maps that can be used to predict parameters such as wind speed up to 240 hours out, to understand the movement of a smoke plume over time.

  • GEOS-5 Weather Maps
    Within the viewer, select the parameter or field of interest, the area of interest, and indicate the forecast time and the forecast lead hour. Selecting "Animate" shows the forecast for the given parameter over the time period indicated. Note that it may take time to load the images to animate. For wind speed near the surface, select 850 as your level (note; 850 hPa is approximately 5000 ft or 1500 m above sea level).

The regional impacts of wildfires include not only burned area, but also changes in runoff patterns and landslide potential. While some areas allow for ground-based measurements of these post-fire impacts, areas that are remote or that have rugged terrain can make ground-based measurements impractical. Remote sensing data provide a means to extend our knowledge in these areas.

A combined MODIS burned area product is acquired by employing 500m MODIS Surface Reflectance imagery coupled with 1 km MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data.

  • Image
    Total burned area as visualized through NASA's FIRMS application.
    Burned Area Data from FIRMS
    Select the burned area and your month of interest (see image below). It can take a few seconds to load.
  • Burned Area Data from Earthdata Search
    Terra and Aqua combined Burned Area data product is a monthly, global gridded 500m product containing per-pixel burned-area and quality information.
  • MODIS Land Surface Reflectance from Earthdata Search
    Adding the Terra/MODIS Bands 7-2-1 layer provides more context; this layer is useful for distinguishing burn scars where burned vegetation shows as red and vegetation is bright green.
  • VIIRS Land Surface Reflectance from Earthdata Search
  • HLS Surface Reflectance from Earthdata Search
  • MODIS Corrected Reflectance Bands 7-2-1 in Worldview
    Worldview Tour provides stories to help you learn more about the visualization tool, the satellite imagery we provide and events occurring around the world. This story takes you on a tour of the Camp Fire event, November 8, 2018 north of Sacramento, California. An example of MODIS Corrected Reflectance, bands 7-2-1, is shown in slide 2.
  • VIIRS Corrected Reflectance Bands M11-I2-I1 in Worldview
    This combination is most useful for distinguishing burn scars from naturally low vegetation or bare soil. Burned areas or fire-affected areas are characterized by deposits of charcoal and ash, removal of vegetation and/or the alteration of vegetation structure. When bare soil becomes exposed, the brightness in Band I1 may increase, but that may be offset by the presence of black carbon residue; the near infrared (Band I2) will become darker, and Band M11 becomes more reflective. When assigned to red in the image, Band M11 will show burn scars as deep or bright red, depending on the type of vegetation burned, the amount of residue, or the completeness of the burn.
  • HLS Surface Reflectance in Worldview
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Illustration of fire intensity versus burn severity (Source: U.S. Forest Service).

Burn severity is the effect of fire on ecosystem properties, often defined by the degree of mortality of vegetation (relating to soil heating, consumption of litter). Using satellite imagery from Landsat or MODIS allows for pre and post-fire comparisons and a change detection approach. One of the most effective ways to discriminate is by generating a normalized burn ratio. To calculate this, using Landsat data, see the Applied Sciences Remote Sensing Training on Introduction to Remote Sensing for Wildfire Applications.

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A fire in western Australia has burned at least 2 million acres of rural land—an area six times the size of Los Angeles. The blaze, about 120 kilometers (80 miles) southeast of Broome, was ignited by lightning on October 11, 2018. This image was acquired by NASA/USGS Landsat 8 Operational Land Imager. Credit: NASA

NDVI provides a means to assess vegetation health in a given area. Very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8). Aqua and Terra's MODIS and VIIRS NDVI data can be accessed via the following.

Research quality data products can be accessed via Earthdata Search; datasets are available as HDF files which can be opened using Panoply.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NRT imagery can be accessed in Worldview.

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Near real-time IMERG Early Run Half-Hourly Image, acquired on May 7, 2020. Credit: NASA.

NASA's Precipitation Measurement Missions (PMM) provide a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

The products from these missions are available individually or have been integrated with a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing a temporal resolution of 30 minutes. TRMM has been integrated into the TRMM Multi-satellite Precipitation Algorithm (TMPA) and GPM into the Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG's multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours for flash flood events, Late = 12 hours for crop forecasting, and Final = 3 months for research with the incorporation of rain gauge data).

Research quality data products can be accessed via Earthdata Search:

  • IMERG from Earthdata Search
    Early, Late and Final precipitation data on the half hour or one day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2000.
  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day or NRT and accumulated rainfall at three hours and one day. Data are in HDF format and can be opened using Panoply. Data are available from 1997.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NASA also has models of precipitation, incorporating satellite information with ground-based data when available. These models are part of the LDAS, which takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates. In addition to IMERG and LDAS, the AMSR2 instrument collects data that indicates the rate at which precipitation is falling on the surface of the ocean.

Near real-time data can be accessed from Worldview:

Daymet is a collection of gridded estimates of daily weather parameters. It is modeled on daily meteorological observations. Weather parameters in Daymet include daily minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii.

Daymet data can be retrieved in a variety of ways, including: Earthdata Search; an ORNL DAAC API; ORNL DAAC tools; and through the LP DAAC AppEEARS.

Knowing the topography of an area is important so that fire managers and emergency management professionals can anticipate areas of risk, including direction and speed of wind, landslide potential and runoff.

Image
An ASTER GDEM image of Mt. Raung and the surrounding area. Image Credit: Land Processes Distributed Active Archive Center

A method for delineating topography is the SRTM. SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The ASTER Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane.

On average, compared to geodetic points over the U.S., SRTM data has a lower root mean square error (RMSE); RMSE is a commonly used method to express vertical accuracy of elevation datasets. Digital elevation model data accuracy is typically very sensitive to vegetation cover. ASTER tends to perform better over certain landcover types.

February 2020, LP DAAC released a new data product, NASADEM, available at 1 arc second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products. The improvements were achieved by reprocessing the original SRTM radar signal data and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

 

Last Updated
Feb 11, 2021