Forecast biases of extratropical cyclones classified by their diabatic heating intensity in operational physics-based and machine learning weather prediction models

Abstract. Extratropical cyclones (ETCs) are strongly influenced by moist processes, rendering the impact of diabatic heating critical for forecast performance. We systematically evaluate short-range (12-hour) forecast biases of wintertime maritime ETCs over the North Atlantic, North Pacific, and Southern Ocean for the period 2023–2024. Employing a cyclone-centred composite framework, cyclones are categorised into strong and weak diabatic heating groups. We compare forecasts from the ECMWF high resolution operational 9-km Integrated Forecasting System (IFS) and the data-driven Artificial Intelligence Forecasting System (AIFS).

Both the higher resolution of IFS and the data-driven AIFS significantly reduce the ETC propagation biases previously identified in ERA5. However, both models exhibit more pronounced errors in cyclones with strong diabatic heating. In the physics-based IFS, while the previous severe dry and cold biases are largely improved, the model still underestimates cyclone intensity and warm sector wind speeds. Furthermore, IFS displays a distinct spiral-shaped positive bias in the 850–500 hPa temperature difference, suggesting a misrepresentation of the vertical distribution and depth of diabatic heating. AIFS generally yields similar but smaller biases in most fields compared to IFS. Despite these improvements, AIFS features a notable physical inconsistency as it demonstrates a weaker mean sea level pressure (MSLP) bias but a stronger 10 m wind bias compared to IFS. This is related to an underestimation of the near-surface ageostrophic wind speed.