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\begin{document}

\title{Evaluation of MODIS and Himawari-8 Low Clouds Retrievals over the
Southern Ocean with In Situ Measurements from the SOCRATES Campaign}



\author[1]{Litai Kang}%
\author[2]{Roger T Marchand}%
\author[3]{William Smith, Jr.}%
\affil[1]{Department of Atmospheric Sciences, University of Washington}%
\affil[2]{University of Washington}%
\affil[3]{NASA Langley Research Center}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\begin{abstract}
Aircraft observations collected during the Southern Ocean Cloud
Radiation Aerosol Transport Experimental Study (SOCRATES) in
January-February of 2018 are used to evaluate cloud properties from
three satellite-imager datasets: (i) the Moderate Resolution Imaging
Spectroradiometer (MODIS) level 2 (collection 6.1) cloud product, (ii)
the CERES-MODIS Edition 4 cloud product, and (iii) the NASA SatCORPS
Himawari-8 cloud product.

Overall the satellite retrievals compare well with the in situ
observations, with little bias and modest to good correlation
coefficients when considering all aircraft profiles for which there are
coincident MODIS observations. The Himawari-8 product does, however,
show a statistically significant mean bias of about 1.2 \selectlanguage{greek}μ\selectlanguage{english}m for effective
radius (r\textsubscript{e}) and 2.6 for optical depth (\selectlanguage{greek}τ\selectlanguage{english}) when applied
to a larger set of profiles with coincident Himawari-8 observations.

The low overall mean-bias in the r\textsubscript{e} retrievals is due in
part to compensating errors between cases that are non- or
lightly-precipitating, with cases that have heavier precipitation.
r\textsubscript{e} is slightly biased high (by about 0.5 to 1.0 \selectlanguage{greek}μ\selectlanguage{english}m) for
non- and lightly-precipitating cases and biased low by about 3 to 4 \selectlanguage{greek}μ\selectlanguage{english}m
for heavily-precipitating cases when precipitation exits near cloud top.
The bias in non- and lightly-precipitating conditions is due to (at
least in part) having assumed a drop size distribution in the retrieval
that is too broad. These biases in the r\textsubscript{e} ultimately
propagate into the retrieved liquid water path and number concentration.%
\end{abstract}%



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