Deep Learning for Post-Processing Ensemble Weather Forecasts

Abstract

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.

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• Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
• Machine learning applications for weather and climate need greater focus on extremes
• Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
• Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks
• Artificial neural networks ensemble methodology to predict significant wave height
• Learning to Assimilate in Chaotic Dynamical Systems
• Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks
• Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products