\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{Quantifying 3D Gravity Wave Drag in a Library of Tropical
Convection-permitting Simulations for Data-driven Parameterizations}



\author[1]{Y. Qiang Sun}%
\author[1]{Pedram Hassanzadeh}%
\author[2]{M. Joan Alexander}%
\author[3]{Christopher G Kruse}%
\affil[1]{Rice University}%
\affil[2]{NorthWest Research Associates, CoRA Office}%
\affil[3]{NorthWest Research Associates}%


\vspace{-1em}



  
  \date{May 28, 2024}


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





\selectlanguage{english}
\begin{abstract}
Atmospheric gravity waves (GWs) span a broad range of length scales. As
a result, the un-resolved and under-resolved GWs have to be represented
using a sub-grid scale (SGS) parameterization in general circulation
models (GCMs). In recent years, machine learning (ML) techniques have
emerged as novel methods for SGS modeling of climate processes. In the
widely-used approach of supervised (offline) learning, the true
representation of the SGS terms have to be properly extracted from
high-fidelity data (e.g., GW-resolving simulations). However, this is a
non-trivial task, and the quality of the ML-based parameterization
significantly hinges on the quality of these SGS terms. Here, we compare
three methods to extract 3D GW fluxes and the resulting drag (GWD) from
high-resolution simulations: Helmholtz decomposition, and spatial
filtering to compute the Reynolds stress and the full SGS stress. In
addition to previous studies that focused only on vertical fluxes by
GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We
build and utilize a library of tropical high-resolution
(\$\textbackslash{}Delta x =3\textasciitilde{}km\$) simulations using
weather research and forecasting model (WRF). Results show that the SGS
lateral momentum fluxes could have a significant contribution to the
total GWD. Moreover, when estimating GWD due to lateral effects,
interactions between the SGS and the resolved large-scale flow need to
be considered. The sensitivity of the results to different filter type
and length scale (dependent on GCM resolution) is also explored to
inform the scale-awareness in the development of data-driven
parameterizations.%
\end{abstract}%



\sloppy




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

