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arXiv · 2506.10868

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

Simon Lang·Martin Leutbecher·Pedro Maciel
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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 (ECMWF). AIFS-CRPS is trained by directly optimising the almost fair continuous ranked probability score (afCRPS). 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.

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Simon Lang, Martin Leutbecher, Pedro Maciel. 2025-06-12. A multi-scale loss formulation for learning a probabilistic model with proper score optimisation. https://arxiv.org/abs/2506.10868

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