Evaluating the skill of AI and physics-based models for February 2020 North Atlantic storm hazards

Abstract The emergence of data-driven weather forecast models provides great promise for producing faster, computationally cheaper weather forecasts, compared to physics-based numerical models. However, while the performance of artificial intelligence (AI) models has been evaluated primarily for average conditions and single extreme weather events, less is known about their ability to capture sequences of extreme events – periods that are usually accompanied by multiple natural hazards. The February 2020 storm series provides a prime example to evaluate the performance of AI models for predicting multiple storm hazards. This event was associated with severe surface impacts including intense surface wind speeds and heavy precipitation, amplified regionally due to the close succession of three extratropical storms. In this study, we compare the performance of data-driven models to physics-based models in forecasting the February 2020 storm series over the United Kingdom. Our results show that, for these case studies, AI models tend to outperform the numerical model in predicting mean sea level pressure (MSLP) on weekly timescales, and, to a lesser extent, surface winds. Nevertheless, certain ensemble members within the physics-based forecast system can perform as well as, or occasionally outperform, the AI models. Moreover, weaker error correlations between atmospheric variables suggest that AI models may overlook physical constraints. This analysis helps to identify gaps and limitations in the ability of data-driven models to be used for multi-hazard warnings, and emphasizes the need to integrate such models with physics-based approaches for reliable impact forecasting.