In this study, we investigate the impact of assimilating Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs) and tropospheric gradients (TGs) on the simulation of Super Typhoon Koinu (2023) using the Weather Research and Forecasting (WRF) model. Four data assimilation experiments are conducted: a control experiment assimilating conventional observations only, experiments separately assimilating ZTDs and tropospheric gradients on top of conventional observations, and a combined experiment assimilating both ZTDs and tropospheric gradients on top of the conventional observations. A two-month assimilation experiment using observations from approximately 250 GNSS stations demonstrates the overall benefit of GNSS data, with reductions in root-mean-square error (RMSE) of up to 60 % in ZTD when both ZTDs and tropospheric gradients are assimilated. Assimilation of tropospheric gradients alone also reduces the ZTD RMSE, demonstrating that gradients provide independent and complementary information beyond the vertically integrated moisture constraint of ZTDs. The impact of Typhoon Koinu is then examined as it approaches Taiwan between 3 and 6 October 2023. Continuous assimilation of GNSS observations improves the representation of the atmospheric moisture field surrounding the cyclone, leading to changes in the spatial distribution of integrated water vapor and the azimuthal moisture asymmetry around the storm. These moisture adjustments produce small but systematic improvements in the environmental deep-layer steering flow and, consequently, modest reductions in typhoon track error after landfall over Taiwan. The combined assimilation of ZTDs and tropospheric gradients also provides the most accurate simulation of cyclone intensity. The results demonstrate that tropospheric gradients complement ZTDs by improving the representation of horizontal moisture variability, thereby enhancing the simulation of the tropical cyclone environment. This study highlights the potential of assimilating multiple GNSS tropospheric products to improve tropical cyclone prediction in numerical weather prediction systems.