Evaluation of the Impact of YunYao GNSS-RO Refractivity Data Assimilation on Typhoon Forecasts

The accuracy of numerical weather prediction largely depends on the quality of the initial conditions. Global Navigation Satellite System radio occultation (GNSS-RO) observations, with their all-weather capability and high vertical resolution, provide significant advantages in reducing initial condition errors. Taking Typhoon BEBINCA in September 2024 as a case study, this study employed the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. By assimilating YunYao GNSS-RO refractivity observations and conducting sensitivity experiments with the WRF Single-Moment 6-class (WSM6) and the Thompson cloud microphysics schemes, we systematically evaluated the impacts of refractivity assimilation on typhoon forecasts. The results indicate that assimilating GNSS-RO refractivity data substantially improves the consistency between the model initial fields and observations, effectively enhancing the analysis quality in the middle and upper troposphere. Sensitivity experiments further reveal that assimilation exerts a positive impact on both typhoon track and central pressure forecasts, with the magnitude of improvements strongly dependent on the choice of cloud microphysics scheme. Specifically, under the Thompson scheme, refractivity assimilation produces more pronounced improvements in central pressure forecasts, reducing mean biases by about 2–5 hPa; under the WSM6 scheme, assimilation is more favorable for track prediction, decreasing landfall position errors by approximately 50 km. This study demonstrates that assimilating YunYao GNSS-RO refractivity observations can provide reliable data support for typhoon prediction, while appropriate selection of physical parameterization schemes is essential for realizing assimilation benefits, offering valuable guidance for improving mesoscale typhoon forecast accuracy.