Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Year: 2024
Type: article
Abstract: . The last few years have witnessed the emergence of data-driven weather forecast models able to compete and in some respects outperform physics-based numerical models. However, recent studies question 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 windspeed extremes globally. We find that data-driven models outperform ECMWF’s physics-based deterministic model in the average prediction of 10 m windspeed and 2 m temperature, and can also compete with the physics-based model in terms of extremes in most regions. However, the choice of best model depends strongly on region, type of extreme and sometimes even lead time. Thus, we conclude that data-driven models may already now be a useful complement to physics-based forecasts in those regions where they display superior tail performance, but that some challenges still need to be overcome before widespread operational implementation can take place.
Source: Geoscientific model development
Authors: Leonardo Olivetti, Gabriele Messori
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,725
OpenAlex ID: W4404143263