loading page

Deep Learning for Improving Numerical Weather Prediction of Rainfall Extremes
  • Philipp Hess,
  • Niklas Boers
Philipp Hess
Free University of Berlin

Corresponding Author:[email protected]

Author Profile
Niklas Boers
Potsdam Institute for Climate Impact Research
Author Profile

Abstract

The accurate prediction of rainfall, and in particular rainfall extremes, remains challenging for numerical weather prediction models. This can be attributed to subgrid-scale parameterizations of processes that play a crucial role in the multi-scale dynamics, as well as the strongly intermittent nature and the highly skewed, non-Gaussian distribution of rainfall. Here we show that a specific type of deep neural networks can learn rainfall extremes from a numerical weather prediction ensemble. A frequency-based weighting of the loss function is proposed to enable the learning of extreme values 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 extremes by factors ranging from two to above six, depending on the event magnitude.