Toward Improved Tropical Cyclone Forecasting: An Optimized Strategy Combining Dynamic Initialization and Bogus Data Assimilation

In numerical weather prediction, dynamic initialization (DI) of tropical cyclones (TCs) and bogus data assimilation (BDA) are two widely used vortex initialization methods, both of which substantially affect the track and intensity of simulated TCs. This study investigated the impact mechanisms of DI and BDA using the Typhoon Regional Assimilation and Prediction System, which is a moving nested configuration of the Weather Research and Forecasting model designed specifically for TC prediction. Sensitivity experiments demonstrated that the DI approach tends to systematically overestimate TC intensity, primarily owing to moisture updates. Adopting BDA can alleviate this overestimation but often introduces underestimation of TC intensity, thereby limiting its applicability. To address these limitations, this study proposed an optimized strategy that leverages the strengths of both methods: BDA is activated when the bogus vortex intensity is close to the observations, whereas DI is used when the bogus vortex intensity is substantially underestimated. To avoid DI‐induced overestimation, full‐variable updates are applied only under strong predicted TC intensification, and DI is deactivated during weakening phases. The optimized strategy, tested on six TC cases, reduced errors in TC track, minimum sea level pressure, and maximum wind speed forecasts by 21.6%, 38.6%, and 33.8%, respectively, relative to a non‐optimized DI and BDA experiment. Improved TC track and intensity predictions also enhanced quantitative precipitation forecast scores across thresholds by 9.6%–54.8%. These results highlight the critical role of accurate vortex initialization for reliable TC forecasting.