Short Range Predictability of Sudden Tropical Cyclone Track Turns: A Multi‐Branch Forecasting Framework With a Steering Sensitive Cross Track Constraint

Abstract Sudden tropical cyclone (TC) track turns remain a major source of short range forecast error over the western North Pacific because small environmental differences can shift both the turning time and the turning location, rapidly amplifying cross track displacement. Here we examine whether these cases are better understood as a branch‐sensitive predictability grouping rather than as generic high‐error samples. To examine this grouping, we use a short range TC track forecasting framework that combines a mixture of experts design, used to represent multiple plausible track branches, with a steering‐sensitive cross track loss term that emphasizes directional errors during turning conditions. The model uses TC history, TC centered ERA5 environmental fields, and infrared imagery, and is trained for 6–24 hr displacement forecasting using 2000–2020 data and evaluated on independent cases from 2021 to 2024. For the full sample, forecast skill remains comparable to the baseline models, whereas the largest gains occur in sudden turn cases. In these cases, the improvement is more evident in turn synchronized diagnostics than in endpoint error alone, indicating that the gain comes mainly from better representation of positional mismatch. Counterfactual diagnostics further show that the improvement is more strongly associated with pressure level environmental fields than with single level surface fields. These results support the interpretation that sudden TC turns constitute a steering sensitive, branch‐dependent forecast grouping whose short range predictability is more strongly associated with mid to upper level circulation reorganization than with slowly varying lower‐boundary conditions.