Tropical Cyclones Data Pathfinder - Find Data
NASA provides datasets, tools, and near real-time data for use in understanding the formation, movement, and impacts of tropical cyclonic storms.
Near real-time IMERG Early Run Half-Hourly Image, acquired on November 12, 2019.
Near real-time IMERG Early Run Half-Hourly Image, acquired on May 7, 2020. Credit: NASA.

The products from the joint NASA/Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) Mission are available individually and also have been integrated with data from a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing precipitation data in 30 minutes (in the case of GPM). The integrated products are the TRMM Multi-satellite Precipitation Analysis (TMPA) and the Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG's multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours, e.g., for flash flood events; Late = 12 hours, e.g., for crop forecasting; and Final = 3 months, with the incorporation of rain gauge data, for research).

Research-quality data products can be accessed using Earthdata Search:

  • TMPA
    Rainfall estimate at 3 hours, 1 day, or near real-time (NRT) and accumulated rainfall at 3 hours and 1 day. Data are in HDF format, and can be opened using the NASA Panoply data viewer. Data are available from 1997.
  • IMERG
    Early, Late, and Final precipitation data on the half-hour or 1-day timeframe. Data are in NetCDF or HDF format, and can be opened using Panoply. Data are available from 2000.

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 called 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.

  • TMPA
  • IMERG: Data are available from 2000-present.

Data can be visualized using the NASA Worldview satellite imagery exploration tool:

  • IMERG Precipitation Rate
  • AMSR2 Precipitation Rate
    The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument collects data that indicate the rate at which precipitation is falling on the surface of the ocean and is measured in millimeters per hour (mm/hr).
  • NLDAS Precipitation Total
  • GLDAS Precipitation Total (data only available through 2010)
    The NLDAS monthly Precipitation Total data are generated through temporal accumulation of the hourly datal; the GLDAS monthly data are generated through temporal averaging of the 3-hourly data. The data are in units of "kg/m2/s," which is equivalent to "mm/s."

The NRT "early run" product at NASA's Global Precipitation Measurement website is generated every half hour with a 6-hour latency from the time of data acquisition:

Daymet is a collection of gridded estimates of daily weather parameters and 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 application programming interface (API), tools developed by NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), and through NASA's Land Processes DAAC (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) tool.

Relative humidity at 2 m above the surface from MERRA-2 visualized in the Prediction of Worldwide Energy Resources Data Access Viewer. The graphs display percent relative humidity for the single point over South Carolina.
Relative humidity at 2 m above the surface from MERRA-2 visualized in the Prediction of Worldwide Energy Resources (POWER) Data Access Viewer. The graphs display percent relative humidity for the single point over South Carolina. Credit: POWER.

One important ingredient for tropical storm formation is high relative humidity values from the ocean surface to the mid-levels of the atmosphere. As this warm, humid air rises, it creates extensive areas of clouds, precipitation, and embedded thunderstorms.

Research-quality data products can be accessed using Earthdata Search:

  • AIRS Relative Humidity
    AIRS data are daily at 1 degree and the Level 3 data products are provided in either the descending (equatorial crossing north to south at 1:30 a.m. mean local time) or ascending (equatorial crossing south to north at 1:30 p.m. mean local time) orbit. Note that the data were acquired only until 2016.
  • MERRA-2 Humidity
    There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface specific humidity, specific humidity at 2 m, and relative humidity.

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 called 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.

The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides solar and meteorological datasets through a responsive web mapping application with the capability for data subsetting, charting, and visualization. The data are GIS-ready.

image
Group for High Resolution Sea Surface Temperature (GHRSST) multi-sensor ultra-high resolution sea surface temperature data acquired January 31, 2016. Credit: NASA's Physical Oceanography DAAC.

While heat energy is stored and mixed throughout the depth of the ocean, the temperature of water at the ocean surface plays a significant role in weather and is a driving force for cyclonic storms. Where the sea surface temperature (SST) is high, relatively large amounts of heat energy and moisture enter the atmosphere, sometimes producing intense storms.

Research-quality data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite can be accessed using 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 called 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.

  • Aqua MODIS SST data
    Data products from MODIS on the Aqua satellite at 4 km resolution are provided at both 8-day and monthly temporal resolutions.

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis dataset, the Multi-Scale Ultra High Resolution Sea Surface Temperature (MUR SST), is now available from June 2002 to present at 1 km spatial resolution. The dataset is based upon nighttime skin and sub-skin observations from several instruments, including satellite and in situ observations.

Giovanni time-averaged map of surface wind speed during Hurricane Katrina, which made landfall on August 29, 2005.
Giovanni time-averaged map of surface wind speed during Hurricane Katrina, which made landfall on August 29, 2005. Credit: NASA.

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of satellites that collect frequent measurements of surface wind speeds in the inner core of tropical cyclones. The satellites use the constant and ubiquitous signals from the Global Positioning Satellite (GPS) system. 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.

Research-quality CYGNSS and MERRA-2 data products can be accessed using Earthdata Search:

  • CYGNSS Wind
    Average wind speed (m/s) and mean square slope (MSS)
  • MERRA-2 Eastern and Northward Wind
    Wind measurements are in meters per second; the file is available as a netCDF and can be opened using NASA's Panoply data viewer. Note that sea level pressure is also available within this same file.

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 called 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.

Data can be visualized using the NASA Worldview satellite imagery exploration tool:

Time averaged map of sea level pressure monthly, August 2005, from MERRA-2 Model, visualized in Giovanni. Credit: NASA.
Time averaged map of sea level pressure monthly, August 2005, from MERRA-2 Model, visualized in Giovanni. Credit: NASA.
Atmospheric pressure at sea level is important for the development of cyclonic storms. Low pressure systems generally produce high winds and warmer air, which are ideal for storm formation. As the pressure in the storm lowers, wind speeds increase. Typhoon Tip, a massive storm that occurred in October 1979, had peak wind speeds of around 300 km/h and was over 1,300 miles wide. This storm had the lowest recorded sea level pressure worldwide at 870 millibars.

Research-quality data products can be accessed using Earthdata Search:

  • MERRA-2 Sea Level Pressure
    Sea level pressure is in Pascal; the file is available in NetCDF format, and can be opened using the NASA Panoply data viewer. Note that eastward wind and northward winds are also available within this same file.

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 called 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.

Cyclone Hazard Frequency and Distribution. Credit: NASA's SEDAC.
SEDAC map showing global cyclone hazard frequency and distribution. Darker colors indicate ares more prone to cyclonic storms, such as the coast of China. Map from the SEDAC Global Cyclone Hazard and Distribution, v1 (1980–2000). Credit: SEDAC.

NASA's Socioeconomic Data and Applications Center (SEDAC) provides a number of datasets on population exposure and vulnerability and flood hazard potential:

SEDAC also has several tools for visualizing population data, like the POPGRID Viewer that enables direct comparison of different population datasets and the Hazards Mapper web mapping application that allows users to estimate the number of people in proximity to a natural disaster and to assess exposure.

The State of the Ocean (SOTO), available through NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), is an interactive web-based tool to generate informative maps, animations, and plots that communicate and promote the discovery and analysis of the state of the oceans. The tool includes data such as sea surface temperature, surface winds, surface currents, sea surface height, soil moisture, and precipitation.

State of the Oceans (SOTO) is an interactive web-based tool to generate informative maps, animations, and plots that communicate and prove the discovery and analysis of the state of the oceans.
Screenshot of the SOTO landing page showing ocean SSTs for July 5, 2020. Red and yellow indicate warmer temperatures; blue and purple indicate cooler temperatures. Credit: PO.DAAC SOTO.
Tropical Cyclone Amphan on 18 May 2020 at 12:10 UTC (Himawari-8/Clean IR)
The Clean Infrared (10.3 um, Band 13) layer from the Advanced Himawari Imager (AHI), as visualized in Worldview, is useful for detecting clouds all times of day and night and is quite useful in retrievals of cloud top height. Credit: NASA Worldview.

Clouds

The joint NASA/NOAA Geostationary Operational Environmental Satellite (GOES)-East satellite is centered on 75.2 degrees W, covering the Conterminous U.S., Canada, Central America, and South America (along with most of the Atlantic Ocean). The joint NASA/NOAA GOES-West satellite is centered on 137.2 degrees W, covering most of the Pacific Ocean, the U.S., most of Canada, Central America, the western half of South America, and parts of Australasia. The Japan Meteorological Agency 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. The data from the red visible layer is used primarily to monitor the evolution of clouds throughout the daylight hours. It is also useful for identifying small-scale features such as river fog/clear air boundaries, or overshooting tops of cumulus clouds. Data are acquired every 10 minutes or more often for mesoscale sectors, with time steps as small as 30 seconds if two mesoscale sectors are overlaid.

GOES and Himiawari-8 imagery can be interactively explored using the NASA Worldview satellite imagery exploration tool:

Cloud Top Temperatures

Infrared light can be used to determine the temperatures of clouds and thunderstorms that make up tropical cyclones. The stronger the storms are, the higher they extend into the troposphere, which results in cold cloud-top temperatures.

    Precipitation

    Sea surface temperature anomalies, September 21, 2020, from the Multiscale Ultrahigh Resolution data product. Visualization from the State of the Ocean (SOTO) tool. Credit: NASA
    Sea surface temperature anomalies, September 21, 2020, from the Multiscale Ultrahigh Resolution data product. Visualization from the State of the Ocean (SOTO) tool. Credit: NASA

    Relative Humidity

    Sea Surface Temperature (SST) 

      Surface Reflectance

      • MODIS Corrected Reflectance Imagery in NASA Worldview
        The MODIS Corrected Reflectance imagery is available only as near real-time imagery. The MODIS Corrected Reflectance algorithm uses 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 Imagery in NASA Worldview
        The VIIRS Corrected Reflectance imagery is available only as near real-time imagery.

      Weather Maps

      NASA's Global Modeling and Assimilation Office provides applications for interactive analysis and visualizations of experimental, climatological data​, like this model of precipitation and sea level pressure for May 8, 2020.
      NASA's Global Modeling and Assimilation Office provides applications for interactive analysis and visualizations of experimental, climatological data, like this model of precipitation and sea level pressure for May 8, 2020. Credit: NASA GMAO.

      NASA's Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System, Version 5 (GEOS-5) model has a series of weather maps that can be used to predict parameters such as precipitation, humidity, wind speed, and temperature up to 240 hours out.

      • 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 5,000 ft/1,500 m above sea level).

      NASA Earth Applied Sciences Disasters program area Tropical Cyclone Dashboard

      The dashboard contains experimental NASA products that may assist in preparing for or responding to a tropical cyclone. Datasets include precipitation, sea surface temperature, radar reflectivity, flood detection, soil moisture, and wind speed.

      Observing Tropical Storms using NASA GIS Data

      NASA's near real-time GIS Tropical Storms Web App and StoryMap provide access to data layers that allow you to study many facets of tropical cyclones including current precipitation from the Integrated Multi-Satellite Retrieval for Global Precipitation Measurement (IMERG) Early Run, global sea surface temperature, and the track of recent hurricanes, cyclones, and typhoons. An interactive companion, the Tropical Storms Web Map, also is available.

      Observing Tropical Storms using NASA GIS Data
      Layers on this map from the GIS Tropical Storms StoryMap include colored lines off the U.S. East Coast and in the Indian Ocean indicating the tracks of past tropical cyclonic storms and ocean colors indicating sea surface temperature. Credit: NASA ArcGIS DAAC Collaboration.

       

      NASA has several products that can be used to assess flooding qualitatively created from data acquired by sensors aboard the MODIS sensors aboard NASA's Terra and Aqua satellites, VIIRS aboard the joint NASA/NOAA Suomi NPP and GOES-20 satellites, and the joint NASA/USGS Landsat series of satellites. However, cloud cover and nighttime conditions can impede data collection by these sensors. During a storm, this is a significant drawback. Once a storm has passed, pre-event and post-event imagery can provide a quantitative assessment of flooding extent and other damage.

      During a storm, Synthetic Aperture Radar (SAR) may provide a better “view,” as it is able to penetrate cloud cover and works in both day and night conditions. SAR data are more complex, requiring a certain level of processing skill. Refer to the Tools for Data Access and Visualization section for information on processing Sentinel SAR data.

      Observations of surface reflectivity from CYGNSS over the southeastern United States and Caribbean. (a) Surface reflectivity observations for the time period Jul 1–Aug 20, 2017, before the hurricane season began. (b) Surface reflectivity observations after Hurricane Harvey (Aug 25–Sep 15, 2017) for the southeastern United States. Observations in the inset of Cuba were recorded in the time period after Hurricane Irma (Sep 8–Sep 30, 2017). (c) Observed change in surface reflectivity after Hurricanes Harvey (s
      Observations of surface reflectivity from CYGNSS over the Southeastern U.S. and the Caribbean. (a) Surface reflectivity observations for the time period July 1–August 20, 2017, before the hurricane season began. (b) Surface reflectivity observations after Hurricane Harvey (August 25–September 15, 2017) for the Southeastern U.S. Observations in the inset of Cuba were recorded in the time period after Hurricane Irma (September 8–30, 2017). (c) Observed change in surface reflectivity after Hurricanes Harvey (Southeastern U.S.) and Irma (Cuba inset). Credit: Image used by permission of Clara Chew, University Corporation for Atmospheric Research.

      NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission is responsive to reflections from standing water and the amount of moisture in the soil, showing promise for mapping flood inundation. Currently flood inundation maps have to be derived from the Delay-Doppler Maps (DDMs) found in the Level 1 datasets. Information on how these data can be used to produce flood inundation maps can be found in research papers, like CYGNSS data map flood inundation during the 2017 Atlantic hurricane season.

      Research-quality data products can be accessed using Earthdata Search or through NASA partner websites:

      Data can be visualized in NASA Worldview (often in near real-time) and through NASA’s experimental Global Flood Mapping and Advanced Rapid Imaging and Analysis (ARIA) projects:

      • VIIRS Corrected Reflectance in Worldview
        Note that a false-color image created by combining bands M11 as red, I2 as green, and I1 as blue is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges.
      • Event Response to Floods at ARIA
        The ARIA Project, a joint effort of the California Institute of Technology and NASA's Jet Propulsion Laboratory, is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response.
      Soil moisture conditions in Texas near Houston, generated by NASA's Soil Moisture Active Passive (SMAP) satellite before and after the landfall of Hurricane Harvey can be used to monitor changing ground conditions due to Harvey's rainfall.
      Soil moisture conditions in Texas near Houston, generated by NASA's Soil Moisture Active Passive (SMAP) satellite before and after the landfall of Hurricane Harvey can be used to monitor changing ground conditions due to Harvey's rainfall. Credit: NASA.

      NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top 5 cm of soil globally, every 2–3 days, at a resolution of 9–36 km. NASA, in collaboration with other agencies, has developed models of soil moisture content, 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 of soil moisture and evapotranspiration.

      Research-quality data products can be accessed using Earthdata Search; datasets are available in HDF5 format, which are also customizable to GeoTIFF.

      ORNL DAAC's 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.

      LP DAAC's AppEEARS offers another option to simply and efficiently extract subsets, transform, and visualize SMAP data products.

      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 called 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.

      NRT imagery can be accessed using NASA Worldview:

      Top image shows a typical night before Hurricane Maria made landfall, based upon cloud-free and low moonlight conditions; the below image is a composite that shows light detected by VIIRS on the nights of September 27 and 28, 2017. The images above show widespread outages around San Juan, including key hospital and transportation infrastructure.
      Top image shows a typical night before Hurricane Maria made landfall, based upon cloud-free and low moonlight conditions; the lower image is a composite that shows light detected by VIIRS on the nights of September 27 and 28, 2017. The September 27–28 image shows widespread power outages around San Juan, including key hospital and transportation infrastructure. Credit: NASA.

      The VIIRS Day/Night Band shows Earth’s surface and atmosphere using a sensor designed to capture low-light emission sources under varying illumination conditions, which provides an assessment of power outages across an area.

      Note that the imagery to the right is not showing raw imagery of light. A team of scientists from NASA’s Goddard Space Flight Center and Marshall Space Flight Center processed and corrected the raw data to filter out stray light from the Moon, fires, airglow, and any other sources that are not electric lights. Their processing techniques also remove as much other atmospheric interference as possible.

      NASA has also developed the Black Marble, a daily calibrated, corrected, and validated product suite, so nightlight data can be used effectively for scientific observations. Black Marble's standard science processing removes cloud-contaminated pixels and corrects for atmospheric, terrain, vegetation, snow, lunar, and stray light effects on the VIIRS Day/Night Band radiances. Black Marble data can be accessed at NASA's Level-1 and Atmosphere Archive and Distribution System DAAC (LAADS DAAC). Black Marble imagery in NASA Worldview is an image composite that was assembled from clear, cloud free images for 2012 and 2016. Black Marble also provides a Nighttime Blue/Yellow Composite, which is a false color composite created using the VIIRS at-sensor radiance and brightness temperatures from the M15 band.

      For more information on the VIIRS DNB and Black Marble products, read the Nighttime Lights Backgrounder.

       

      Return to the Disaster Data Pathfinder

      Last Updated
      Feb 11, 2021

      Topics

      Atmosphere
      Atmospheric Water Vapor | Atmospheric Winds | Cloud Properties | Cloud Top Temperature | Clouds | Humidity | Precipitation | Surface Winds | Tropical Cyclones | Water Vapor Indicators | Weather Events
      Ocean
      Ocean Pressure | Ocean Temperature | Sea Level Pressure | Sea Surface Temperature
      Human Dimensions
      Disaster Response | Hazard Mitigation/Planning | Hurricanes | Natural Hazards | Social Behavior | Tropical Cyclones
      Land Surface
      Soil Moisture/Water Content | Soils

      Data Archives

      ASDC
      ASF DAAC
      GES DISC
      LAADS DAAC
      LP DAAC
      ORNL DAAC
      PO.DAAC
      SEDAC

      Sensors

      AIRS
      AMSR2
      ETM+
      GMI
      MODIS
      OLI
      Sentinel-1 C-band SAR
      VIIRS

      Technologies