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\begin{document}

\title{Dust Aerosol Retrieval Over the Oceans with the MODIS/VIIRS Dark Target
algorithm. Part I: Dust Detection}



\author[1]{Yaping Zhou}%
\author[2]{Robert Levy}%
\author[3]{Lorraine Remer}%
\author[4]{Shana Mattoo}%
\author[5]{Yingxi Shi}%
\author[6]{Chenxi Wang}%
\affil[1]{University of Maryland Baltimore County}%
\affil[2]{NASA-Goddard Space Flight Center}%
\affil[3]{University of Maryland}%
\affil[4]{SSAI and NASA/GSFC}%
\affil[5]{NASA Goddard Space Flight Center}%
\affil[6]{University Of Maryland Baltimore County}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
To prepare for implementation of a new aerosol retrieval specifically
designed for dust aerosol over ocean in the operational Dark Target (DT)
algorithms for the Moderate-resolution Imaging Spectrometer (MODIS) and
Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we
focus on the challenge of detecting dust. We first survey the literature
on existing dust detection algorithms and then develop an innovative
algorithm that combines near-UV (deep blue), visible, and thermal
infrared (TIR) wavelength spectral tests. The new detection algorithm is
applied to Terra and Aqua MODIS granules and compared with other dust
detection possibilities from existing MODIS products. Quantitative
evaluation of the new dust detection algorithm is conducted using both a
collocated AERONET - MODIS dataset and collocated CALIPSO -- MODIS
dataset. From comparison with both AERONET and CALIOP measurements, we
estimate the new dust detection algorithm detects about 30\% of weakly
dusty pixels and more than 80\% of heavily dusty pixels, with false
detections in the range of 1-2\%. The very low false detection rate is
particularly noteworthy in comparison with existing literature. Compared
with the dust flag currently available as part of the MODIS cloud mask
product (MOD35/MYD35), and dust classification based on commonly used
thresholds with AOD and AE, the new dust detection algorithm finds more
dusty pixels and fewer false detections.%
\end{abstract}%



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