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Statistical post-processing of global ensemble weather forecasts is revisitedby leveraging recent developments in machine learning. Verification of pastforecasts is exploited to learn systematic deficiencies of numerical weatherpredictions in order to boost post-processed forecast performance. Here, weintroduce PoET, a post-processing approach based on hierarchical transformers.PoET has 2 major characteristics: 1) the post-processing is applied directly tothe ensemble members rather than to a predictive distribution or a functionalof it, and 2) the method is ensemble-size agnostic in the sense that the numberof ensemble members in training and inference mode can differ. The PoET outputis a set of calibrated members that has the same size as the original ensemblebut with improved reliability. Performance assessments show that PoET can bringup to 20% improvement in skill globally for 2m temperature and 2% forprecipitation forecasts and outperforms the simpler statisticalmember-by-member method, used here as a competitive benchmark. PoET is alsoapplied to the ENS10 benchmark dataset for ensemble post-processing andprovides better results when compared to other deep learning solutions that areevaluated for most parameters. Furthermore, because each ensemble member iscalibrated separately, downstream applications should directly benefit from theimprovement made on the ensemble forecast with post-processing.

Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers