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\begin{document}

\title{Physics-Incorporated Framework for Emulating Atmospheric Radiative
Transfer and the Related Network Study}



\author[1]{Yichen Yao}%
\author[2]{Xiaohui Zhong}%
\author[3]{Yongjun Zheng}%
\author[1]{Zhibin Wang}%
\affil[1]{Alibaba}%
\affil[2]{Damo Academy, Alibaba Group}%
\affil[3]{Nanjing University of Information Science and Technology}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
The calculations of atmospheric radiative transfer are among the most
time-consuming components of the numerical weather prediction (NWP)
models. Therefore, using deep learning to achieve fast radiative
transfer has become a popular research direction. We propose a
physics-incorporated framework for the radiative transfer model
training, in which the thermal relationship between fluxes and heating
rates is encoded as a layer of the network so that the energy
conservation can be satisfied. Based on this framework, we compared
various types of neural networks and found that the model structures
with global receptive fields are more suitable for the radiative
transfer problem, among which the Bi-LSTM model has the best
performance.%
\end{abstract}%



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