Deep Learning for Improving Numerical Weather Prediction of Rainfall
- Philipp Hess,
- Niklas Boers
Free University of Berlin
Corresponding Author:firstname.lastname@example.orgAuthor Profile
Potsdam Institute for Climate Impact ResearchAuthor Profile
AbstractThe 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.