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\begin{document}

\title{Training warm-rain bulk microphysics schemes using super-droplet
simulations}



\author[1]{Sajjad Azimi}%
\author[1]{Anna Jaruga}%
\author[1]{Emily K De Jong}%
\author[2]{Sylwester Arabas}%
\author[3]{Tapio Schneider}%
\affil[1]{Caltech}%
\affil[2]{AGH University of Krakow, Poland}%
\affil[3]{California Institute of Technology}%


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  \date{June 06, 2024}


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\begin{abstract}
Cloud microphysics is a critical aspect of the Earth's climate system,
which involves processes at the nano- and micrometer scales of droplets
and ice particles. In climate modeling, cloud microphysics is commonly
represented by bulk models, which contain simplified process rates that
require calibration. This study presents a framework for calibrating
warm-rain bulk schemes using high-fidelity super-droplet simulations
that provide a more accurate and physically based representation of
cloud and precipitation processes. The calibration framework employs
ensemble Kalman methods including ensemble Kalman inversion (EKI) and
unscented Kalman inversion (UKI) to calibrate bulk microphysics schemes
with probabilistic super-droplet simulations. We demonstrate the
framework's effectiveness by calibrating a single-moment bulk scheme,
resulting in a reduction of data-model mismatch by more than
\$75\textbackslash{}\%\$ compared to the model with initial parameters.
Thus, this study demonstrates a powerful tool for enhancing the accuracy
of bulk microphysics schemes in atmospheric models and improving climate
modeling.%
\end{abstract}%



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