This study employs the storm surge numerical model originally developed for the Bohai Sea. In contrast to the previous work, the current simulation focuses on the South China Sea and severe weather events, with a case study conducted through the numerical simulation of the storm surge induced by Typhoon “Mangkhut” on 15–17 September 2018. The typhoon wind field serves as a critical forcing factor influencing simulation accuracy. Therefore, five different wind fields were employed as driving conditions: the ERA5 reanalysis wind field (EWF), the parameterized Jelesnianski (JWF) and Holland (HWF) wind fields, and two hybrid wind fields (JEWF and HEWF) combining EWF with JWF and HWF, respectively. And the adjoint data assimilation method was applied to invert a physically more consistent wind stress drag coefficient (C_D). The results indicate the model effectively reproduced this dynamic process. After assimilation, the simulated water levels showed significantly improved agreement with measurements, with errors reduced by 50% (EWF), 47% (JWF), 48% (JEWF), 26% (HWF), and 42% (HEWF), respectively. And JWF not only exhibited a smaller MAE than HWF but also yielded a more reasonable C_D, demonstrating its superior performance in this typhoon case. The hybrid strategy further reduced errors to 0.14 m, effectively mitigating the limitations of individual wind fields. This study offers a new perspective on adjoint assimilation-based numerical modeling of storm surges in the South China Sea.

This study applied an adjoint data assimilation model capable of integrating wind fields to investigate a temperate storm surge event in the Bohai Sea region during October 18 to 21, 2024. Based on in situ water level measurements from five tide gauge stations, the model simulated the spatial distributions of water levels under different wind stress drag coefficients (C_D) schemes driven by reanalysis wind fields and interpolated wind fields. The results demonstrated that the scheme without the adjoint data assimilation exhibited relatively low accuracy. Upon integrating the adjoint data assimlation method, the errors of the reanalysis wind fields were reduced by 44%, while those of the interpolated wind fields experienced a 74% decrease in error magnitude. Overall, this study provides a reference for enhancing the accuracy of water level predictions during storm surge events.