Atmos. Chem. Phys., 12, 9679-9686, 2012
www.atmos-chem-phys.net/12/9679/2012/
doi:10.5194/acp-12-9679-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
Discrimination of biomass burning smoke and clouds in MAIAC algorithm
A. Lyapustin1, S. Korkin2, Y. Wang3, B. Quayle4, and I. Laszlo5
1Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
2Universities Space Research Association, Columbia, Maryland, USA
3University of Maryland Baltimore County, Baltimore, Maryland, USA
4USDA Forest Service, Salt Lake City, Utah, USA
5NOAA/NESDIS/STAR, Camp Springs, Maryland, USA

Abstract. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the "smoke test" to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 μm as compared to 0.47–0.67 μm due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements. This paper provides the algorithm detail and illustrates its performance on two examples of wildfires in US Pacific North-West and in Georgia/Florida of 2007.

Citation: Lyapustin, A., Korkin, S., Wang, Y., Quayle, B., and Laszlo, I.: Discrimination of biomass burning smoke and clouds in MAIAC algorithm, Atmos. Chem. Phys., 12, 9679-9686, doi:10.5194/acp-12-9679-2012, 2012.
 
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