Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts.
@article{, author={Peter Grönquist and Chengyuan Yao and Tal Ben-Nun and Nikoli Dryden and Peter Dueben and Shigang Li and Torsten Hoefler}, title={{Deep Learning for Post-Processing Ensemble Weather Forecasts}}, journal={Philosophical Transactions of the Royal Society A}, year={2021}, month={Feb.}, volume={379}, number={2194}, publisher={The Royal Society}, source={http://www.unixer.de/~htor/publications/}, }
