Radar Data Assimilation with EnKF-IAU: Impacts on Dynamical Structure and Short-term Prediction of Landfalling Typhoon Bebinca (2024)

Landfalling tropical cyclones (LTCs) undergo rapid structural adjustments and complex nonlinear interactions in coastal regions, making the short-term prediction of heavy rainfall and damaging winds particularly challenging. Conventional intermittent data assimilation often introduces dynamical imbalances into the analysis fields, which may further deteriorate subsequent forecasts. This study investigates the landfall process of Typhoon Bebinca (2024) and systematically evaluates a set of ensemble-based assimilation experiments conducted within an Incremental Analysis Update (IAU) framework, incorporating multiple observation types, including radar reflectivity, Doppler radial velocity, and surface measurements. The results show that the IAU technique, through the gradual application of analysis increments within a four-dimensional time window, effectively suppresses initialization shocks, alleviates spurious dynamical imbalance, and preserves flow-dependent coordination. The IAU-based framework efficiently retains observational information, optimizes vortex structure, intensifies the warm core, and promotes the formation of a vertically coherent subsidence column within the eye region, thereby strengthening the secondary circulation. In addition, the IAU scheme also helps establish a more consolidated and axisymmetric moisture core, accompanied by a sea-level pressure field with smoother and dynamically coherent gradient structures, indicating a more physically balanced thermodynamic–dynamic coupling. These balanced analyses translate into more accurate forecasts of track, intensity evolution, and landfall-induced precipitation. These balanced analyses are subsequently translated into more accurate forecasts of typhoon track, intensity, and landfall-related precipitation. Overall, the IAU-enhanced ensemble assimilation system substantially improves the physical consistency of storm analyses and significantly increases the short-term predictability of LTC track, rainfall, and wind hazards over coastal urban regions.