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\begin{document}

\title{}



\author[1]{Xingyu Zhu}%
\author[1]{Xiaoming Shi}%
\affil[1]{Division of Environment and Sustainability, Hong Kong University of Science and Technology}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\begin{abstract}
Data-driven parameterization schemes have gained much attention in the
field of atmospheric science nowadays. We design an ideal case using the
Barotropic Vorticity Equation (BVE) model with periodic shear forcing
and a deep learning (DL) model. The trained BVE model can greatly
improve its initial forecast lead time with the data-driven
parameterization scheme. However, the challenge is the significant
amount of time required to employ the model, which is a hurdle for
practical applications. Thus, this research aims at enhancing the
model's efficiency while minimizing any negative impact on its
performance. Here we propose three methods, compressing the amount of
model parameters (CNNhk), compressing the amount of training data (SGM),
and combining both of them (CFS). Through these three methods and
utilizing the solver-in-the-loop (SOL) method, we successfully reduce
the model's runtime while preserving its initial performance. By
improving the effectiveness of our model, we believe it can contribute
to the development of more efficient data-driven parameterization
schemes and inspire further explorations.%
\end{abstract}%



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