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Journal Article

Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall

Authors
/persons/resource/philipp.hess

Hess,  Philipp
Potsdam Institute for Climate Impact Research;

/persons/resource/Niklas.Boers

Boers,  Niklas
Potsdam Institute for Climate Impact Research;

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Citation

Hess, P., Boers, N. (2022): Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall. - Journal of Advances in Modeling Earth Systems, 14, 3, e2021MS002765.
https://doi.org/10.1029/2021MS002765


Cite as: https://publications.pik-potsdam.de/pubman/item/item_28022
Abstract
The accurate prediction of rainfall, and in particular of the heaviest rainfall events, remains challenging for numerical weather prediction (NWP) models. This may be due to subgrid-scale parameterizations of processes that play a crucial role in the multi-scale dynamics generating rainfall, as well as the strongly intermittent nature and the highly skewed, non-Gaussian distribution of rainfall. Here we show that a U-Net-based deep neural network can learn heavy rainfall events from a NWP ensemble. A frequency-based weighting of the loss function is proposed to enable the learning of heavy rainfall events in the distributions' tails. We apply our framework in a post-processing step to correct for errors in the model-predicted rainfall. Our method yields a much more accurate representation of relative rainfall frequencies and improves the forecast skill of heavy rainfall events by factors ranging from two to above six, depending on the event magnitude.