Midlatitude Cyclone Intensity Biases in Machine Learning Weather Prediction Models

Forecasting the location and intensity of strong winds associated with midlatitude cyclones remains a key challenge due to their substantial societal and environmental impacts. In this study, we conditionally evaluate the ability of numerical weather prediction (NWP) models and machine learning weather prediction (MLWP) models to represent wind structures linked to these cyclones. Using a feature‑based tracking approach applied to a large sample of Northern Hemisphere cyclone events, we compare how different modelling frameworks capture cyclone evolution, including track, intensity, and near‑surface wind characteristics.

Our analysis shows that MLWP models can reproduce broad aspects of cyclone behaviour, such as large‑scale track evolution, with skill comparable to established operational NWP forecasting systems at medium-range lead times. However, we also identify systematic differences in how these models represent cyclone intensity and associated wind extremes. In particular, MLWP models tend to underestimate key high‑impact features, such as minimum pressure and peak near‑surface winds, relative to dynamical NWP forecasts.

These findings highlight both the promise and current limitations of MLWP systems for predicting midlatitude cyclone hazards. Understanding these behaviours provides guidance for future model development and for the use of ML‑based forecasts in operational and risk‑focused applications.