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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 12 | Copyright
Atmos. Meas. Tech., 4, 2823-2836, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Dec 2011

Research article | 22 Dec 2011

A critical examination of spatial biases between MODIS and MISR aerosol products – application for potential AERONET deployment

Y. Shi1, J. Zhang1, J. S. Reid2, E. J. Hyer2, T. F. Eck3, B. N. Holben3, and R. A. Kahn3 Y. Shi et al.
  • 1Department of Atmospheric Sciences, University of North Dakota, Grand Forks, ND, USA
  • 2Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA
  • 3NASA GSFC, Greenbelt, MD, USA

Abstract. AErosol RObotic NETwork (AERONET) data are the primary benchmark for evaluating satellite-retrieved aerosol properties. However, despite its extensive coverage, the representativeness of the AERONET data is rarely discussed. Indeed, many studies have shown that satellite retrieval biases have a significant degree of spatial correlation that may be problematic for higher-level processes or inverse-emissions-modeling studies. To consider these issues and evaluate relative performance in regions of few surface observations, cross-comparisons between the Aerosol Optical Depth (AOD) products of operational MODIS Collection 5.1 Dark Target (DT) and operational MODIS Collection 5.1 Deep Blue (DB) with MISR version 22 were conducted. Through such comparisons, we can observe coherent spatial features of the AOD bias while sidestepping the full analysis required for determining when or where either retrieval is more correct. We identify regions where MODIS to MISR AOD ratios were found to be above 1.4 and below 0.7. Regions where lower boundary condition uncertainty is likely to be a dominant factor include portions of Western North America, the Andes mountains, Saharan Africa, the Arabian Peninsula, and Central Asia. Similarly, microphysical biases may be an issue in South America, and specific parts of Southern Africa, India Asia, East Asia, and Indonesia. These results help identify high-priority locations for possible future deployments of both in situ and ground based remote sensing measurements. The Supplement includes a kml file.

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