\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{Physically-Informed Video Inpainting: A Deep Learning Approach for
Historical Weather Reconstruction}



\author[1]{Yannis Valentin Schmutz}%
\author[2]{Noemi Imfeld}%
\author[2]{Stefan Brönnimann}%
\author[1]{Erik Graf}%
\affil[1]{Bern University of Applied Sciences}%
\affil[2]{University of Bern}%


\vspace{-1em}



  
  \date{June 01, 2024}


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





\selectlanguage{english}
\begin{abstract}
We investigate the applicability of deep learning methods for
reconstructing daily weather data. Inspired by video inpainting, we
propose a novel method, WeRec3D, which utilizes a three-dimensional
convolutional neural network. Our approach was developed iteratively by
evaluating six modeling improvement techniques.\\
The resulting method reduces the validation error to 48\% compared to
the baseline.\\
Additionally, we demonstrate the impact of the spatial distribution of
observations on reconstruction accuracy and propose a potential
integration with the analogue resampling method.\\
WeRec3D is trained and validated in a self-supervised manner using
ERA5's surface temperature and pressure data over Europe.\\
On a hold-out set from 1950 to 1954, the validation results in an MAE of
1.11 \selectlanguage{ngerman}°C and 199 Pa.\\
As a case study, we reconstruct the 1807 heat wave and validate it using
a leave-one-out method in space.\\
Compared to the original data, the reconstructed time series exhibit a
correlation of at least 0.91, with a maximum normalized RMSE and
standard deviation delta of 0.58 and 0.51 respectively.\\
To the best of our knowledge, this is the first study to investigate
weather reconstruction using deep learning algorithms, proposing video
inpainting as a novel approach for reconstructing missing weather
information.%
\end{abstract}%



\sloppy




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

