Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 12
Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7425-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-7425-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 12
Model experiment description paper
 | 
06 Dec 2021
Model experiment description paper |  | 06 Dec 2021

Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model

Alexei Belochitski and Vladimir Krasnopolsky

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Short summary
There is a lot interest in using machine learning (ML) techniques to improve environmental models by replacing physically based model components with ML-derived ones. The latter ordinarily demonstrate excellent results when tested in a stand-alone setting but can break their host model either outright when coupled to it or eventually when the model changes. We built an ML component that not only does not destabilize its host model but is also robust with respect to substantial changes in it.
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