Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts

Abstract:Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challenging. This study compares statistical and machine-learning-based methods for post-processing ECMWF 2-m temperature and 10-m wind speed forecasts at observed and unobserved stations in Germany. We consider EMOS-based approaches, distributional regression networks, Transformers, and graph neural networks under both limited and extended predictor settings. For temperature, we also investigate linear forecast combinations and propose an altitude-aware linear pool (ALP). The results show that post-processing improves upon the raw ensemble in most settings, but no single method performs best across all variables, station groups, and evaluation metrics. The proposed ALP provides a small but significant improvement over the standard linear pool at unobserved locations.

Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)

arXiv:2609.07512 [cs.LG]

(or arXiv:2609.07512v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.07512 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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