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\begin{document}

\title{Revisiting Machine Learning Approaches for Short- and Longwave Radiation
Inference in Weather and Climate Models, Part I: Offline Performance}



\author[1]{Guillaume Bertoli}%
\author[2]{Firat Ozdemir}%
\author[2]{Sebastian Schemm}%
\author[3]{Fernando Perez-Cruz}%
\affil[1]{ETHZ}%
\affil[2]{ETH Zurich}%
\affil[3]{Swiss Data Science Center, ETH Zurich}%


\vspace{-1em}



  
  \date{June 07, 2024}


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\begin{abstract}
As climate modellers prepare their code for kilometre-scale global
simulations, the computationally demanding radiative transfer
parameterization is a prime candidate for machine learning (ML)
emulation. Because of the computational demands, many weather centres
use a reduced spatial grid and reduced temporal frequency for radiative
transfer calculations in their forecast models. This strategy is known
to affect forecast quality, which further motivates the use of ML-based
radiative transfer parameterizations. This paper contributes to the
discussion on how to incorporate physical constraints into an ML-based
radiative parameterization, and how different neural network (NN)
designs and output normalisation affect prediction performance. A random
forest (RF) is used as a baseline method, with the European Centre for
Medium-Range Weather Forecasts (ECMWF) model ecRad, the operational
radiation scheme in the Icosahedral Nonhydrostatic Weather and Climate
Model (ICON), used for training. Surprisingly, the RF is not affected by
the top-of-atmosphere (TOA) bias found in all NNs tested (e.g., MLP,
CNN, UNet, RNN) in this and previously published studies. At lower
atmospheric levels, the RF can compete with all NNs tested, but its
memory requirements quickly become prohibitive. For a fixed memory size,
most NNs outperform the RF except at TOA. The most accurate emulator is
a recurrent neural network architecture that closely imitates the
physical process it emulates. The shortwave and longwave fluxes are
normalized to reduce their dependence on the solar angle and surface
temperature respectively. The model are, furthermore, trained with an
additional heating rates penalty in the loss function.%
\end{abstract}%



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