Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers

Understand

Statistical post-processing 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 post-processed forecast performance.
• Here, we introduce PoET, a post-processing approach based on hierarchical transformers.
• PoET has 2 major characteristics: 1) the post-processing 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.