Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

Autor: L. Olivetti, G. Messori
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Geoscientific Model Development, Vol 17, Pp 7915-7962 (2024)
Druh dokumentu: article
ISSN: 1991-959X
1991-9603
DOI: 10.5194/gmd-17-7915-2024
Popis: The last few years have witnessed the emergence of data-driven weather forecast models capable of competing with – and, in some respects, outperforming – physics-based numerical models. However, recent studies have questioned the capability of data-driven models to provide reliable forecasts of extreme events. Here, we aim to evaluate this claim by comparing the performance of leading data-driven models in a semi-operational setting, focusing on the prediction of near-surface temperature and wind speed extremes globally. We find that data-driven models mostly outperform ECMWF’s physics-based deterministic model in terms of global RMSE for forecasts made 1–10 d ahead and that they can also compete in terms of extreme weather predictions in most regions. However, the performance of data-driven models varies by region, type of extreme event, and forecast lead time. Notably, data-driven models appear to perform best for temperature extremes in regions closer to the tropics and at shorter lead times. We conclude that data-driven models may already be a useful complement to physics-based forecasts in regions where they display superior tail performance but note that some challenges still need to be overcome prior to operational implementation.
Databáze: Directory of Open Access Journals
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