Abstract Tianmu‐1 commercial 23‐satellite constellation provides over 30,000 daily Global Navigation Satellite System (GNSS) Radio Occultation (RO) profiles, significantly expanding atmospheric profiling observations. This study evaluates the impact of assimilating Tianmu‐1 RO data in the Gridpoint Statistical Interpolation data assimilation & Weather Research and Forecasting (WRF) system on enhancing the forecast of Typhoon Gaemi (2024). Over the 120‐hr forecasting period, track errors were reduced by 20%–40%, with marked improvements beyond 48 hr. Further analysis indicates that the improvement in track prediction is related to improved storm inner‐core thermal structure and the large‐scale steering flow. Although no other satellite data were included in the assimilation, the Tianmu‐enhanced forecasts exhibited slightly higher accuracy than those from operational track prediction by the National Centers for Environmental Prediction's Global Forecast System. These findings demonstrate the growing role of commercial GNSS RO data in numerical weather prediction, potentially supplementing or reducing reliance on satellite brightness temperature assimilation.
Yang et al. (Sat,) studied this question.
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