Synergistic Impact of Spaceborne and Ground‐Based GNSS Observations on Tropical Cyclone Forecasting: A Case Study of Cyclone Kirrily

Abstract Accurate forecasting of tropical cyclones (TCs) is critical for mitigating coastal hazards. Integrating multi‐source observations to improve initial conditions plays a key role in enhancing TC forecast skill in numerical weather prediction (NWP). Although previous studies have demonstrated the value of Global Navigation Satellite System (GNSS) observations in NWP, the benefits of jointly assimilating spaceborne and ground‐based GNSS observations for TC prediction remains insufficiently explored. This study innovatively implements the synergistic assimilation of spaceborne GNSS radio occultation (RO) refractivity and ground‐based GNSS zenith total delay (ZTD) observations using a regional NWP model and evaluates its impact on the Southern Hemisphere severe TC Kirrily. Data from the Chinese commercial meteorological satellite Tianmu‐1 (TM‐1), the Global Data Assimilation System (GDAS), and Geoscience Australia (GA) are applied. Results show that synergistic assimilation improves the simulated TC structure and surrounding environment, yielding more accurate lower‐to‐middle tropospheric thermodynamic and kinematic forecasts. Verification against ERA5 shows reduced root‐mean‐square errors (RMSEs) for temperature, winds, and moisture, with maximum reductions of 8.1% at 500 hPa, 5.7% at 750 hPa, and 8.0% at 700 hPa, radiosonde verification exhibits a similar pattern. For TC prediction, joint assimilation reduces Kirrily's track errors after 30 hr and improves minimum sea level pressure (MSLP) forecasts, while overestimating maximum surface wind speed (MWS) during early intensification. It also yields a more realistic hydrometeor distribution and enhances rainfall prediction, particularly for light‐to‐moderate precipitation. These findings highlight the potential of exploiting complementary GNSS observations to improve TC forecasting and strengthen early warning capabilities for associated hazards.