Do AI models predict storm impacts as accurately as physics-based models? A case study of the February 2020 storm series over the North Atlantic

TL;DR

This study compares AI and physics-based weather models during the February 2020 storm series, finding AI models often outperform numerical models in predicting certain variables but may lack physical constraints, highlighting the need for integrated approaches.

Contribution

It provides a comparative analysis of AI and physics-based models for storm impact prediction during a complex storm series, revealing strengths and limitations of data-driven approaches.

Findings

AI models outperform numerical models in weekly mean sea level pressure predictions.

Physics-based ensemble members can match or outperform AI models.

AI models may overlook physical constraints due to weaker variable correlations.

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 have been evaluated primarily for average conditions and single extreme weather events, less is known about their capability to capture sequences of extreme events, states that are usually accompanied by multiple hazards. The storm series in February 2020 provides a prime example to evaluate the performance of AI models for storm impacts. This event was associated with high 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. We show that on weekly timescales, AI models tend to outperform the numerical model in predicting mean sea level pressure (MSLP), 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 impact warnings, and emphasizes the need to integrate such models with physics-based approaches for reliable impact forecasting.