Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events

Abstract The forecast accuracy of machine learning (ML) weather prediction models is improving rapidly, leading many to speak of a “second revolution in weather forecasting.” With numerous methods being developed and limited physical guarantees offered by ML models, there is a critical need for a comprehensive evaluation of these emerging techniques. While this need has been partly fulfilled by benchmark datasets, they provide little information on rare and impactful extreme events or on compound impact metrics, for which model accuracy might degrade due to misrepresented dependencies between variables. To address these issues, we compare ML weather prediction models (GraphCast, Pangu-Weather, and FourCastNet) and ECMWF’s high-resolution forecast system (HRES) in three case studies: the 2021 Pacific Northwest heatwave, the 2023 South Asian humid heatwave, and the North American winter storm in 2021. We find that ML weather prediction models locally achieve similar accuracy to HRES on the record-shattering Pacific Northwest heatwave but underperform when aggregated over space and time. However, they forecast the compound winter storm substantially better. We also highlight structural differences in how the errors of HRES and the ML models build up to that event. The ML forecasts lack important variables for a detailed assessment of the health risks of the 2023 humid heatwave. Using a possible substitute variable, prediction errors show spatial patterns with the highest danger levels over Bangladesh being underestimated by the ML models. Generally, case study–driven, impact-centric evaluation can complement existing research, increase public trust, and aid in developing reliable ML weather prediction models. Significance Statement With the performance of machine learning–based weather forecasting models improving rapidly, thorough analyses are needed to ensure that their forecasts are accurate and reliable before deploying them in operational settings. Existing evaluations often reduce forecast performance to a few metrics, potentially obscuring rare but systematic errors. This is especially problematic for high-impact extreme events, which, by definition, are rare in the data but often substantially affect society. In a detailed analysis of three extreme events, we observe that, although machine learning (ML) models generally outperform the best physics-based numerical weather prediction (NWP) model on benchmark datasets, they do not consistently do so for the studied extreme events or compound impact metrics and lack some impact-relevant variables.