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.
@article{arxiv.2506.10868,
title = {A multi-scale loss formulation for learning a probabilistic model with proper score optimisation},
author = {Simon Lang and Martin Leutbecher and Pedro Maciel},
journal= {arXiv preprint arXiv:2506.10868},
year = {2025}
}