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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 4 | Copyright
Atmos. Meas. Tech., 6, 949-969, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Apr 2013

Research article | 12 Apr 2013

Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa

Y. Shi1, J. Zhang1, J. S. Reid2, E. J. Hyer2, and N. C. Hsu3 Y. Shi et al.
  • 1Department of Atmospheric Science, University of North Dakota, Grand Forks, ND, USA
  • 2Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA
  • 3NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) collection 5.1 (c5.1) aerosol optical depth (AOD) data were analyzed and evaluated for the first time from an independent research group using eight years of Terra (2000–2007) and Aqua (2002–2009). Uncertainties in the DB AOD were identified and studied, and our results show that the performance of DB c5.1 is strongly dependent on surface albedo and aerosol microphysics. Using data with only "very good" quality assurance, the root-mean-square error (RMSE) of the DB Terra (Aqua) AOD is 0.24 (0.19) when validated against AERONET. Expanding upon the uncertainty analysis, the potential of applying the DB products for aerosol assimilation was explored. Empirical corrections and quality assurance procedures were developed for North Africa and the Arabian Peninsula to create a data assimilation (DA)-quality DB product. After applying those procedures, the RMSE is reduced by 18.1% (18.2%) for Terra (Aqua) DB data. Prognostic error models of 0.069 + 0.175 × AODTerra_DB with no noise floor and 0.048 + 0.182 × AODAqua_DB with a noise floor of 0.104 were found for DA-quality Terra and Aqua DB data, respectively. These procedures were also applied to two months of DB collection 6 (c6) AOD data, and reductions in RMSE were found, indicating that the algorithms developed for c5.1 data are applicable to c6 data to some extent.

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