Abstract
Abstract
Dynamic imbalances in model initialization severely constrain the predictability of the rapid intensification (RI) and abrupt track recurvature of tropical cyclones (TCs). In this study, the effectiveness of an incremental analysis update (IAU)‐based four‐dimensional variational (4DVar) data assimilation scheme in forecasting Typhoons Doksuri and Khanun in 2023 was investigated. By reducing spin‐up impact, the mean track errors obtained with the IAU scheme stabilized below 60 km, and the intensity forecast errors were consistently reduced compared to the control experiment. These improvements can be mechanistically attributed to a superior representation of the fractured subtropical high and the internal asymmetric dynamic structure of TCs, which resulted in critical steering flow correction. Consequently, this dynamical enhancement yielded a 60% relative improvement in forecast skill for heavy rainfall (≥50 mm) at the 72‐hr lead time, indicating the potential benefit of improved TC initialization for heavy precipitation prediction.