\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{Non-Gaussian Detection using Machine Learning with Data Assimilation
Applications}



\author[1]{Michael Goodliff}%
\author[2]{Steven James Fletcher}%
\author[3]{Anton Kliewer}%
\author[3]{Andrew S. Jones}%
\author[3]{John M Forsythe}%
\affil[1]{University of Colorado}%
\affil[2]{Cooperative Institute for Reserach in the Atmosphere}%
\affil[3]{Colorado State University}%


\vspace{-1em}



  
  \date{May 27, 2024}


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





\selectlanguage{english}
\begin{abstract}
In most data assimilation and numerical weather prediction systems, the
Gaussian assumption is prevalent for the behaviour of the random
variables/errors that are involved. At the Cooperative Institute for
Research in the Atmosphere (CIRA) theory has been developed for
different forms of variational data assimilation schemes that enables
the Gaussian assumption to be relaxed. For certain variable types, a
lognormally distributed random variable can be combined in a mixed
Gaussian-lognormal distribution to better capture the interactions of
the errors of different distributions. However, assuming that a
distribution can change in time, then developing techniques to know when
to switch between different versions of the data assimilation schemes
becomes very important. Given this ability to change the formulation of
the data assimilation system enable us to select the more optimal scheme
for the different distributed situations.\\
In this paper, we present results with a machine learning technique (the
support vector machine) to switch between data assimilation methods
based on the detection of a change in the Lorenz 1963 model's \$z\$
component's probability distribution. Given the machine learning
technique's detection/prediction of a change in the distribution, then
either a Gaussian or a mixed Gaussian-lognormal 3DVar based cost
function is used to minimise the errors in this period of time. It is
shown that switching from a Gaussian 3DVar to a lognormal 3DVar at
lognormally-distributed parts of the attractor improves the data
assimilation analysis error compared to using one distribution type for
the entire attractor.%
\end{abstract}%



\sloppy




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

