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Extreme Heat Data Pathfinder

Information on extreme heat events including what it is, who is most vulnerable, and how to avoid heat-related deaths.

Information on extreme heat events. Credit: Center for Disease Control (CDC)

According to the United States Global Change Research Program, heat waves are occurring more frequently in major cities across the nation. Heat waves are periods of abnormally hot and/or humid weather, lasting a few days to weeks at a time. In the 1960s, major cities in the U.S. experienced, on average, about two heat waves per year. In the 2010s, that number rose to more than six heat waves per year. Not only are cities experiencing more extreme heat events, the seasons are also lasting longer, on average 47 days longer than in 1960. Even under different climate models and emission scenarios, results indicate that extreme heat events worsen.

Humidity is an important factor in heat index assessment. When the humidity is high, water does not evaporate as easily and so it becomes difficult for the body to cool off through sweating. The heat index incorporates both temperature and humidity and is used to determine public health warnings for areas experiencing heat waves. The public health impacts of heat waves include exhaustion or heat stroke, and even death. According to the Center for Disease Control, extreme heat results in about 600 deaths per year in the U.S, with the elderly, very young, outdoor workers, and people with mental illness and chronic diseases at higher risk.

In monitoring heat waves, it's important to access long-term data records to assess abnormalities from the norm. Using remote sensing data can be an asset in determining climate trends, as several satellite platforms have been acquiring data over many years. For example, the Terra satellite has been acquiring land surface temperature data since 2000. With consistent and continuous data coverage, reliable temperature and humidity anomalies can be assessed.

Urban heat islands play a role in extreme heat events. Cities tend to have higher temperatures than outlying more rural areas; this is due primarily with the differences in radiative and thermal properties of varying surfaces, especially impervious surfaces such as buildings, pavement, etc., as well as the spatial distribution of water, soils, vegetation, and manmade surfaces.

Find Temperature Data

Find Temperature Data

​Surface air temperature, measured in Kelvin,​ from the Atmospheric Infrared Sounder (AIRS), May 9, 2020, visualized in Panoply.

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 (see Tools for Data Access and Visualization). 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).

Air Surface Temperature

Research-quality 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:

Land Surface Temperature

Satellite images show the relationship between the characteristics of a landscape, and day and night surface skin temperature. Heavily forested areas remain relatively cool throughout the day, while barren and arid areas can be tens of degrees warmer. These images were acquired in the early morning and afternoon of July 6, 2011.

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.

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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Find Humidity Data

Find Humidity Data

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.

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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Find Land Cover Data

Find Land Cover Data

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

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.

Land Cover

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.

Vegetation Greenness

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.

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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Find Weather Maps

Find 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.

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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Find Socioeconomic Data

Find Socioeconomic Data

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.

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.

NOAA and the Centers for Disease Control and Prevention (CDC) have a Future Heat Events and Social Vulnerability web app, which centralizes a broad range of information, from socioeconomic status, household composition, minority status, and housing with urban heat island data.

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Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AppEEARS | MODIS/VIIRS Subsetting Tools Suite

Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA's Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the Earth science disciplines. Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files, with customization for select data collections.

Screenshot of the Search Earthdata site.

In the project area, for some datasets, you can customize your granule. You can reformat the data and output as HDF, NetCDF, ASCII, KML, or GeoTIFF format. You can also choose from a variety of projection options. Lastly, you can subset the data, obtaining only the bands that are needed.

Earthdata Search customization tools diagram.

Panoply

Files in HDF and NetCDF format can be viewed in Panoply, a cross-platform application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.

Giovanni

Giovanni is an online environment for the display and analysis of geophysical parameters. There are many options for analysis. The following are the more popular ones.

  • Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
  • Map animations are a means to observe spatial patterns and detect unusual events over time.
  • Area-averaged time series are used to display the value of a data variable that has been averaged from all the data values acquired for a selected region for each time step.
  • Histogram plots are used to display the distribution of values of a data variable in a selected region and time interval.

For more detailed tutorials:

  • Giovanni How-To's on GES DISC's YouTube channel.
  • Data recipe for downloading a Giovanni map in NetCDF format, and converting its data to quantifiable map data in the form of latitude-longitude-data value ASCII text.

Worldview

NASA's EOSDIS Worldview visualization application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now." This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS). Worldview now includes nine geostationary imagery layers from GOES-East, GOES-West and Himawari-8 available at ten minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables the visualization of the differentiation between air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. Post-hurricane image shows widespread outages around San Juan, including key hospital and transportation infrastructure.

Worldview Suomi NPP/VIIRS nighttime lights comparison image showing power outages caused by Hurricane Irma in September 2017. The right image (acquired 1 September 2017) shows the island before Hurricane Irma. The left image (acquired 9 September 2017) shows power outages across island after Hurricane Irma. Interactively explore this image in NASA Worldview. Image: NASA Worldview.

AppEEARS

AppEEARS, from LP DAAC, offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available: point samples for geographic coordinates and area samples for spatial areas via vector polygons.

Performing Area Extractions

After choosing to request an area extraction, you will be taken to the Extract Area Sample page where you will specify a series of parameters that are used to extract data for your area(s) of interest.

Spatial Subsetting

Define your region of interest in one of these three ways:

  • Upload a vector polygon file in shapefile format (you can upload a single file with multiple features or multipart single features). Files in .shp, .shx, .dbf, or .prj format must be zipped into a file folder to upload.
  • Upload a vector polygon file in GeoJSON format (can upload a single file with multiple features or multipart single features).
  • Draw a polygon on the map by clicking on the Bounding box or Polygon icons (single feature only).

Select the date range for your time period of interest.

Specify the range of dates for which you wish to extract data by entering a start and end date (MM-DD-YYYY) or by clicking on the Calendar icon and selecting dates a start and end date in the calendar.

Adding Data Layers

Enter the product short name (e.g., MOD09A1, ECO3ETPTJPL), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching your query will be generated. Select the layer(s) of interest to add to the Selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AppEEARS.

Extracting an area in AppEEARS

Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF4

If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF4 is selected, outputs will be grouped into files in .nc format by product and by feature.

If GeoTIFF is selected, you must select a projection

Interacting with Results

Once your request is completed, from the Explore Requests page, click the View icon in order to view and interact with your results. This will take you to the View Area Sample page.

The Layer Stats plot provides time series boxplots for all of the sample data for a given feature, data layer, and observation. Each input feature is renamed with a unique AppEEARS ID (AID). If your feature contains attribute table information, you can view the feature attribute table data by clicking on the Information icon to the right of the Feature dropdown. To view statistics from different features or layers, select a different AID from the Feature dropdown and/or a different layer of interest from the Layer dropdown.

Interpreting Results in AppEEARS

Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or NetCDF4 files.

MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several MODIS and VIIRS Subset Tools for subsetting data.

  • With the Global Subset Tool, you can request a subset for any location on earth, provided as GeoTIFF and in text format, including interactive time-series plots and more. Users specify a site by entering the site's geographic coordinates and the area surrounding that site, from one pixel up to 201 x 201 km. From the available datasets, you can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/Long. You will need to register for an Earthdata Login to request data.
  • With the Fixed Subsets Tool, you can download pre-processed subsets for 3000+ field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites. It includes sites from networks such as National Ecological Observatory Network, Forest Global Earth Observatory network, Phenology Camera network, and Long Term Ecological Research Network that are of relevance to the biodiversity community.
  • With the Web Service, you can retrieve subset data (in real-time) for any location(s), time period, and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into users' workflow.

Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

Top image: The Global Subsets Tool enables users to download available products for any location on Earth. Bottom image: The Fixed Sites Subsets Tool provides spatial subsets for established field sites for site characterization and validation of models and remote sensing products. Image: NASA ORNL DAAC.

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Published July 29, 2020

Page Last Updated: Nov 22, 2021 at 12:56 PM EST