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\begin{document}

\title{Evaluation of neural network emulations for radiation parameterization
in cloud resolving model}



\author[1]{Soonyoung Roh}%
\author[1]{Hwan-Jin Song}%
\affil[1]{National Institute of Meteorological Sciences, Korea Meteorological Administration}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\begin{abstract}
This study evaluated the forecast performance of neural network
(NN)-based radiation emulators with 300 and 56 neurons developed under
the cloud-resolving simulation. These emulators are 20--100 times
cheaper to employ than the original parameterization and express
evolutionary features well for 6 hrs. The results suggest that the
frequent use of an NN emulator can improve not only computational speed
but also forecasting accuracy in comparison to the infrequent use of
original radiation parameterization, which is commonly used for speedup
but can induce numerical instability as a result of imbalance with other
processes. The forecast error of the emulator results was much improved
in comparison with that for infrequent radiation runs with similar
computational cost. The 56-neuron emulator results were even more
accurate than the infrequent runs, which had a computational cost five
times higher. The speed and accuracy advantages of radiation emulators
can be utilized for weather forecasting.%
\end{abstract}%



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