Extreme Heat Data Pathfinder - Find Data

Abnormally hot and/or humid weather lasting a few days to weeks at a time are occurring more frequently in major cities across the world. These events can have detrimental impacts on public health. NASA data can aid with forecasting and monitoring extreme heat events.
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Surface air temperature, measured in Kelvin, from the Atmospheric Infrared Sounder (AIRS), May 9, 2020, visualized in Panoply.

Temperature is useful for assessing changes in weather and climate patterns that are critical for monitoring and responding to extreme heat events. By calculating the average temperature over a range of time, typically about 30 years, and comparing the forecasted high temperature over the forthcoming days to that average, one can determine if temperatures are indeed abnormal for that time period. An anomaly analysis or difference map is necessary to do this and can be created in Giovanni and Panoply. Giovanni provides monthly and seasonal average maps, as well as the time-averaged map for the month or given time period of investigation. The file in NetCF format can then be downloaded and opened in Panoply, where you can create a difference map with the "combine plot" option (see the Earthdata webinar Create Difference Maps for NASA Data with Panoply, Giovanni and Excel for more information on this process).

Research-quality air surface temperature data products can be accessed via Earthdata Search:

  • AIRS Surface Air Temperature from Earthdata Search
    NASA's Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite gathers infrared energy emitted from Earth's surface and atmosphere globally every day. AIRS data are daily, 8-day, and monthly 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. local time) or ascending (equatorial crossing South to North at 1:30 p.m. local time) orbit. When you open the file in HDF format (in a program like Panoply or QGIS), you will see an ascending option and a descending option each with SurfAirTemp.
  • MERRA-2 Temperature from Earthdata Search
    The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface skin temperature, the air temperature at 2 m, and the air temperature at 10 m.

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.

Data, often in near real-time (NRT), are available for visualization in Worldview:

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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 temperatures are in part affected by the albedo, or the ability to reflect radiation, of a surface; dark surfaces, like asphalt, concrete, and brick, tend to have a low albedo and so absorb more of the sun's heat, resulting in higher temperatures. Surface temperatures vary more than air temperatures during the day and are more pronounced after sunset due to the slow release of heat from impervious surfaces.

Land surface temperature research-quality data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (aboard the Aqua and Terra satellites), the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard Terra, and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can be accessed directly from Earthdata Search. In addition, the North American Land Data Assimilation System (NLDAS) monthly climatology dataset, accessible from Earthdata Search, contains a series of land surface parameters simulated from the Noah land-surface model, from 1980-2009.

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

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

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.

  • NLDAS Surface Temperature in Giovanni
    There are a variety of different options including hourly and monthly data. North American Land Data Assimilation System (NLDAS) data are land model output files from 1979 to present. There is also a monthly climatology dataset, which contains a series of land surface parameters simulated from the Noah land-surface model, from 1980-2009.

Data can be visualized in Worldview:

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. MODIS Land Surface Temperature is also available through LP DAAC's geospatial web map service. For information on accessing the data within a GIS program, view the Land Geospatial Services within the GIS Data Pathfinder.

 

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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.

Humidity must be factored in when determining the heat index for an area. For example, if the air temperature is 96°F and the relative humidity is 65%, the heat index, or how hot it feels, is 121°F. In this example, without factoring in humidity, a heat advisory would never be issued, although it feels way above normal temperatures. Heat advisory, watches, and warnings vary across the country, especially for areas that are not used to dangerous heat conditions. Refer to the National Weather Service for a general rule of thumb.

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

  • AIRS Relative Humidity from Earthdata Search
    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. local time) or ascending (equatorial crossing South to North at 1:30 p.m. local time) orbit. Note that the data were acquired only until 2016.
  • MERRA-2 Humidity from Earthdata Search
    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, 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.

Data, often in NRT, can be visualized in Worldview:

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.

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Day and night surface temperature comparisons for a variety of different landcover types.

Urban heat islands are caused by the albedo of surfaces and infrastructure, the amount of vegetative cover, large bodies of water, and anthropogenic heat, that being emitted by vehicles, industrial facilities, etc. Landcover plays a critical role in monitoring urban environments for excess heat.

Terra and Aqua MODIS Land Cover Type data product provides global land cover types at yearly intervals. The product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. This layer defines land cover type based on the International Geosphere-Biosphere Programme classification scheme.

MODIS Land Cover Type is also available through LP DAAC's geospatial web map service. For information on accessing the data within a GIS program, view the Land Geospatial Services within the GIS Data Pathfinder.

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These images from the NASA/USGS satellite Landsat show the cooling effects of plants on New York City’s heat. On the left, areas of the map that are dark green have dense vegetation. Notice how these regions match up with the dark purple regions—those with the coolest temperatures—on the right. Image credit: Maps by Robert Simmon, using data from the Landsat Program.
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 near-infrared (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. The enhanced vegetation index (EVI) minimizes canopy-soil variations and improves sensitivity over dense vegetation conditions. Vegetation products from MODIS and Suomi NPP VIIRS can be accessed in various ways.

Research quality surface reflectance data products can be accessed directly via Earthdata Search; datasets are available as HDF files but are, in some cases, customizable to GeoTIFF.

LP DAAC's Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers the ability to extract subsets, transform, and visualize MODIS and VIIRS vegetation-related data products. NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) subsetting tools provide a means to simply and efficiently access MODIS and VIIRS vegetation-related data products as well.

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 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 data can be accessed via Worldview:

  • MODIS NDVI in Worldview
    This dataset has a spatial resolution of 250 m and the temporal resolution is an 8-day product, updated daily. 16-day and monthly data are also available within Worldview.
  • MODIS EVI in Worldview
    This dataset is monthly at 1 km spatial resolution. Rolling 8-day and 16-day data are also available within Worldview.

MODIS NDVI is also available through geospatial web map services. For information on accessing the data within a GIS program, view the Biosphere Geospatial Services within the GIS Data Pathfinder.

 

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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 Goddard Earth Observing System, Version 5 (GEOS-5) model assimilates data from a variety of observations for each Earth System component. GEOS-5 has a series of weather maps which can be used to produce a 240-hour/10-day forecast of parameters, such as precipitation, humidity, wind speed, and temperature.

  • GEOS-5 Weather Maps
    Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour. 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 those variables near the surface, make sure to select 850 as your level.
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Global urban heat island dataset, from NASA's Socioeconomic Data and Applications Center (SEDAC), estimates the difference between land surface temperatures (LST) in urban areas and surrounding rural areas. LSTs are derived from Aqua MODIS 8-day composite LST data for a 40-day timespan. Credit: SEDAC.

Heat-related deaths are preventable, but prevention requires a knowledge of where vulnerable populations exist and what interventions are needed in those communities. For example, the urban heat island effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. In addition, socioeconomic status may limit a person's ability to mitigate extreme heat. Increasing frequency of heat events and other natural disasters may lead to migration and a change in population composition.

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

Last Updated
Oct 19, 2021