Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall
Philipp Hess, Niklas Boers
Abstract
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.