\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{Improving machine learning-based weather forecast post-processing with
clustering and transfer learning}



\author[1]{Xiaomeng Huang}%
\author[2]{Yuwen Chen}%
\author[1]{Yi Li}%
\author[2]{Yue Chen}%
\author[1]{Chi Yan Tsui}%
\author[1]{Xing Huang}%
\author[1]{Mingqing Wang}%
\author[1]{Jonathon S Wright}%
\affil[1]{Tsinghua University}%
\affil[2]{Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China}%


\vspace{-1em}



  
  \date{May 21, 2024}


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





\selectlanguage{english}
\begin{abstract}
Machine learning has been widely applied in numerical weather
prediction, but the incorporation of new observational sites into models
trained on stations with long historical records remains a challenge.
Here we propose a post-processing framework consisting of three machine
learning methods: station clustering with K-means, temperature
prediction based on decision trees, and transfer learning for
newly-built stations. We apply this framework to post-processing
forecasts of surface air temperature at 301 weather stations in China.
The results show significant reductions (as much as
39.4\%\textasciitilde{}20.0\%) in the root-mean-square error of
operational forecasts at lead times as long as 7 days. Moreover, the use
of transfer learning to incorporate new stations improves forecasts at
the new site by 36.4\% after only one year of data collection. These
results demonstrate the potential for clustering and transfer learning
to boost existing applications of machine learning techniques in weather
forecasting.%
\end{abstract}%



\sloppy




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

