\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[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{U-Net Segmentation for the Detection of Convective Cold Pools From Cloud
and Rainfall Fields}



\author[1]{Jannik Hoeller}%
\author[2]{Romain Fiévet}%
\author[1]{Jan O Haerter}%
\affil[1]{Leibniz Centre for Tropical Marine Research}%
\affil[2]{Copenhagen University}%


\vspace{-1em}



  
  \date{May 18, 2024}


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





\selectlanguage{english}
\begin{abstract}
Convective cold pools (CPs) are known to mediate the interaction between
convective rain cells and thereby help organize thunderstorm clusters,
in particular mesoscale convective systems and extreme rainfall events.
Unfortunately, the observational detection of CPs on a large scale has
so far been hampered by the lack of relevant large-scale nearsurface
data. Unlike numerical studies, where high-resolution near-surface
fields of relevant quantities such as virtual temperature and winds are
available and frequently used to detect cold pools, observational
studies mainly identify CPs based on surface time series. Since research
vessels or weather stations measure these time series locally, the
characterization of cold pools from observations is limited to regional
or station-based studies. To eventually enable studies on a global
scale, we here develop and evaluate a methodology for the detection of
CPs that relies only on data that (i) is globally available and (ii) has
high spatio-temporal resolution. We trained convolutional neural
networks to segment CPs in cloud and rainfall fields from
high-resolution cloud resolving simulation output. Such data is not only
available from simulations, but also from geostationary satellites that
fulfill both (i) and (ii). The networks make use of a U-Net
architecture, a common choice for image segmentation due to its strength
in learning spatial correlations at different scales. Based on cloud and
rainfall fields only, the trained networks systematically identify CP
pixels in the simulation output. Our methodology may thus open for
reliable global CP detection from space-borne sensors. As it also
provides information on the spatial extent and the relative positioning
of CPs over time, our method may offer new insight into the role of CPs
in convective organization.%
\end{abstract}%



\sloppy




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

