A multi‐scale loss formulation for learning a probabilistic model with proper score optimisation

Abstract We assess the impact of a multi‐scale loss formulation for training probabilistic machine‐learned weather forecasting models. The multi‐scale loss is tested in AIFS‐CRPS, a machine‐learned weather forecasting model developed at the European Centre for Medium‐Range Weather Forecasts. AIFS‐CRPS is trained by directly optimising the almost fair continuous ranked probability score. The multi‐scale loss better constrains small‐scale variability without negatively impacting forecast skill. This opens up promising directions for future work in scale‐aware model training.