Enhancing Tropical Cyclone Prediction Through AI Weather Model Ensemble Forecasts and Multi‐Source Data Integration

This is the authors' abstract. We don't add key points for this paper.

Tropical cyclone (TC) forecasting is essential for disaster preparedness, yet accurate intensity prediction remains challenging, particularly during rapid intensification. This study proposes TianXing‐TC, a lightweight probabilistic post‐processing framework that leverages ensemble forecasts from the AI weather prediction model TianXing and integrates multi‐source TC‐related information. The framework first generates perturbed TianXing ensemble forecasts and extracts TC‐centered environmental trajectories along the predicted storm tracks. A TC Correction Network then fuses these environmental trajectories with geostationary satellite cloud‐pattern imagery and storm‐state statistical factors to predict maximum sustained wind, minimum sea‐level pressure, longitude, and latitude up to 96 hr ahead. Compared with the raw TianXing ensemble, TianXing‐TC substantially reduces systematic intensity underestimation and improves track forecasts, particularly at longer lead times. Homogeneous comparisons with official forecasts show that TianXing‐TC consistently reduces intensity forecast errors and achieves competitive track forecast skill, with the largest long‐lead track improvements in the western North Pacific basin. Stratified verification further indicates useful short‐range skill during rapid‐intensification events, although rapid intensity changes remain underestimated. Integrated‐gradient analyses and case studies reveal physically plausible attribution patterns related to environmental flow, convective organization, and storm‐state persistence. These results suggest that AI weather model generated ensemble trajectories, when combined with satellite and statistical information, can provide an efficient complementary approach for TC forecast post‐processing.