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\begin{document}

\title{A Machine Learning Bias Correction of Large-scale Environment of Extreme
Weather Events in E3SM Atmosphere Model}



\author[1]{Shixuan Zhang}%
\author[2]{Bryce E Harrop}%
\author[3]{L. Ruby Leung}%
\author[4]{Alexis-Tzianni Charalampopoulos}%
\author[4]{Benedikt Barthel}%
\author[2]{Wenwei Wenwei Xu}%
\author[5]{Themistoklis Sapsis}%
\affil[1]{Pacific Northwest National Laboratory (DOE)}%
\affil[2]{Pacific Northwest National Laboratory}%
\affil[3]{PNNL}%
\affil[4]{MIT}%
\affil[5]{Massachusetts Institute of Technology}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\begin{abstract}
Large-scale dynamical and thermodynamical processes are common
environmental drivers of extreme weather events. However, such
large-scale environmental conditions often display systematic biases in
climate simulations, posing challenges to evaluating extreme weather
events and associated risks in current and future climate. In this
paper, a machine learning (ML) approach was employed to bias correct the
large-scale wind, temperature, and humidity simulated by the E3SM
atmosphere model at \$\textbackslash{}sim 1\^{}\textbackslash{}circ\$
resolution. The usefulness of the proposed ML approach for extreme
weather analysis was demonstrated with a focus on three extreme weather
events, including tropical cyclones (TCs), extratropical cyclones
(ETCs), and atmospheric rivers (ARs). We show that the ML model can
effectively reduce climate bias in large-scale wind, temperature, and
humidity while preserving their responses to imposed climate change
perturbations. The bias correction is found to directly improve the
water vapor transport associated with ARs, and the representations of
thermodynamical flows associated with ETCs. When the bias-corrected
large-scale winds are used to drive a synthetic TC track forecast model
over the Atlantic basin, the resulting TC track density agrees better
with that of the TC track model driven by observed winds. In addition,
the ML model insignificantly interferes with the mean climate change
signals of large-scale storm environments as well as the occurrence and
intensity of three extreme events. This study suggests that the proposed
ML approach can be used to improve the downscaling of extreme weather
events by providing more realistic large-scale storm environments
simulated by low-resolution climate models.%
\end{abstract}%



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