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Dust Aerosol Retrieval Over the Oceans with the MODIS/VIIRS Dark Target algorithm. Part II: Non-Spherical Dust Model
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  • Yaping Zhou,
  • Robert Levy,
  • Lorraine Remer,
  • Shana Mattoo,
  • William Reed Espinosa
Yaping Zhou
University of Maryland Baltimore County

Corresponding Author:[email protected]

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Robert Levy
NASA-Goddard Space Flight Center
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Lorraine Remer
University of Maryland, Baltimore County
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Shana Mattoo
SSAI and NASA/GSFC
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William Reed Espinosa
Goddard Space Flight Center
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Abstract

The Dark-target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate-resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering-angle-dependent biases in its retrievals of AOD, Angstrom Exponent (AE) and Fine Mode Fraction (FMF) for dust aerosols. Following a two-step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in ‘Part I’), in this ‘Part II’, we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET-identified dusty pixels. The application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error (EE) from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering-angle-dependent AE bias is largely eliminated. In testing with wo full months of data (April and July), the new retrieval reduces the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced.