Exploring NASA Aerosol Data in the Classroom

NEO gives access to georeferenced NASA satellite imagery and provides the means for easy export into other browsers (like GoogleEarth) or analytic tools (like ImageJ or ICE). The site also contains the MODIS Blue Marble, which is a series of monthly true-color global composite images.

NASA's Earth Sciences Division supports a major global climate research program integrating data from 16 Earth-observing satellites, the research of about 750 scientists worldwide, and a robust data processing and distribution system to improve understanding of how Earth's climate system works. One emphasis in this program is aerosols and how they affect Earth's climate and weather patterns. Aerosols are "wild cards" in the climate system in that they can both act to warm and cool Earth; and they can both suppress and enhance cloud formation and rainfall. Moreover, aerosols can cause respiratory and other health problems in humans. Thus, understanding their sources and impacts is an important part of NASA's research. Benefits of this research are realized by both scientists and society (through better air quality monitoring).

This data can be used to teach the following topics and skills in climate science and environmental science.

• What aerosols are
• Natural and anthropogenic sources of aerosols
• How aerosols affect Earth's climate & weather patterns

• Using data to visualize the temporal and spatial extent of various aerosol types
• Using data to visualize how aerosols affect Earth's planetary albedo

Native data products (in HDF-EOS format) are processed into graphic images in popular formats, including JPEG, PNG, and GeoTIFF. The images are available at varying temporal and spatial resolutions globally and regionally for the time spanning March 2000 to present. Initial focus is on Terra and Aqua MODIS data.

In NEO, users can thematically select the data they want using the "Atmosphere" pop-down menu. The Blue Marble image is NEO's default view, so its selection is simple. Users can also search by date or region.

Users can download images and open them in ImageJ for analysis. Doing a histogram of the images will show how aerosols "brighten" the scene from the satellite's perspective by scattering more sunlight back to space, thus demonstrating the ability of aerosols to cool the surface.

Image analysis software such as ImageJ ( This site may be offline. ) is useful in investigating this dataset. For more open-ended, ongoing analysis of aerosols, Google Earth (more info) or the Image Composite Editor are available. The NEO interface includes options for importing the image data into these latter options.

Aerosol data are routinely collected by NASA's Terra and Aqua MODIS sensors every day over most of the globe.

There are several issues of note in the MODIS aerosol data set. First, the sensor requires good surface contrast to retrieve aerosol measures, so it cannot derive aerosol optical depth over bright surfaces such as snow and ice, sun glint on water, or bright desert landscapes. In terms of validation of the data set, the MODIS aerosol data products show excellent agreement with surface based and in situ measures, so the error margins are quite small, well understood, and documented.

There are quite a few feature articles about aerosols in NASA's Earth Observatory, including:

• A New IDEA in Air Quality Monitoring
• Clouds are Cooler than Smoke
• Smoke's Surprising Secret
• Savannah Smog
• When the Dust Settles
• From the Dust Bowl to the Sahel
• Volcanoes and Climate Change
• Changing our Weather One Smokestack at a Time
• Every Cloud Has a Filthy Lining

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NASA NEO Natural Hazards: Dust and Smoke
This NEO Natural Hazards page gives access to timely images of dust and smoke events with interpretive captions.

• NEO Aerosol Optical Thickness: This interactive online "experiment" asks students to observe actual satellite data and images, form their own hypotheses, and draw conclusions about issues surrounding aerosols.
• The GLOBE program has an excellent aerosol protocol which could be complemented using this dataset.

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