\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Seeking guidance from active cloud observations to improve climate model
subcolumn generators}



\author[1]{Lazaros Oreopoulos}%
\author[2]{Nayeong Cho}%
\author[3]{Dongmin Lee}%
\author[4]{Matthew Lebsock}%
\author[2]{Zhibo Zhang}%
\affil[1]{NASA-GSFC}%
\affil[2]{UMBC}%
\affil[3]{Morgan State University}%
\affil[4]{JPL}%


\vspace{-1em}



  
  \date{May 04, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Our objective is to test and improve cloud subcolumn generators used for
greater realism of scales in the radiation schemes and satellite
simulators GCMs. For this purpose, we use as guidance water content
fields from active observations by the CloudSat radar (CPR) and the
CALIPSO lidar (CALIOP). Cloud products from active sensors while
suffering significant sampling and coverage drawbacks have the advantage
of resolving both horizontal and vertical variability which is what the
generators are designed to produce. Our first order goal is to test the
ability of the generators to deliver realistic 2D cloud extinction
(cloud optical thickness) fields using, as in GCMs, limited
domain-averaged information. Our reference 2D cloud extinction fields
fully resolving horizontal (along the track of the satellites) and
vertical variability come from combining CloudSat's 2B-CWC-RVOD (liquid
clouds) and CALIPSO-enhanced 2C-ICE (ice clouds) products. The combined
fields were improved by introducing a simple scheme to fill liquid cloud
extinction values identified as missing by comparing with coincident 2D
(phase-specific) cloud masks provided by the CALIPSO-enhanced
2B-CLDCLASS-LIDAR CloudSat product. Our presentation will demonstrate
the substantial improvements for low clouds brought by the filling
scheme through comparisons with MODIS-Aqua cloud fraction distributions
expressed in terms of joint cloud top pressure -- cloud optical
thickness histograms. Beyond global comparisons, the nature of the
improvements become clearer when comparing mean joint histograms
segregated by MODIS Cloud Regime (CR): improvement is by design superior
for MODIS CRs dominated by low clouds. With the improved 2D extinction
fields at hand, we test the skill of two subcolumn generators, one used
in the COSP satellite simulator package, and one with more sophisticated
cloud overlap implemented in the GEOS global model, to reproduce joint
histograms that are statistically similar to the observed counterparts
described above (as interpreted by COSP's MODIS simulator). Our main
comparison metrics are the Euclidean distance between observed and
generator-produced global or near-global mean joint histograms, and the
statistics of Euclidean distances calculated for individual scenes. One
full year of data is used to assess whether the more sophisticated cloud
generator produces clouds with greater realism in 2D cloud variability.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

