Building a physics-constrained, fast and stable machine learning-based radiation emulator (Guillaume Bertoli, ETH Zurich)

29.03.2022 14:00

Radiative transfer in the atmosphere, which describes the evolution of radiation emitted by the Sun, the Earth's surface, clouds, and greenhouse gases, is an essential component of climate and weather modeling. In climate models, the transfer of radiation is approximated by parameterizations. The current operational radiative transfer solver in the Icosahedral Nonhydrostatic Weather and Climate Model (ICON) is ecRad. It is an accurate radiation parameterization but remains computationally expensive. Therefore, the radiation solver is only run on a reduced spatial grid, which can affect prediction accuracy. In this project, we are trying to develop a radiative transfer solver improved by machine learning to speed up the computation without loss of accuracy. Our research focuses on two methods: random forests and physics-informed neural networks. We continue to call ecRad at constant though significantly reduced time intervals and on a reduced spatial grid thereby using it as a regularizer while reducing computation costs. The underlying idea is to avoid unphysical climate drifts and to support the generalization capabilities of the machine learning method.

Lieu

Bâtiment: Conseil Général 7-9

Room 1-05, Séminaire d'analyse numérique

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