Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers

Abstract

Abstract Statistical postprocessing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in order to boost postprocessed forecast performance. Here, we introduce postprocessing of ensembles with transformers (PoET), a postprocessing approach based on hierarchical transformers. PoET has two major characteristics: 1) the postprocessing is applied directly to the ensemble members rather than to a predictive distribution or a functional of it, and 2) the method is ensemble-size agnostic in the sense that the number of ensemble members in training and inference mode can differ. The PoET output is a set of calibrated members that has the same size as the original ensemble but with improved reliability. Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2-m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark. PoET is also applied to the ENS-10 benchmark dataset for ensemble postprocessing and provides better results when compared to other deep learning solutions that are evaluated for most parameters. Furthermore, because each ensemble member is calibrated separately, downstream applications should directly benefit from the improvement made on the ensemble forecast with postprocessing.