Positive Impacts of Tianmu-1 RO Data Assimilation on Tropical Cyclone Forecasts and the Non-negligible Influence of Altostratus Clouds on RO Data Quality

Global Navigation Satellite System Radio Occultation (GNSS RO) observations are increasingly important for improving atmospheric profiling and numerical weather prediction (NWP), especially in cloudy, moisture-rich tropical environments where other satellite observations are often degraded. This study presents two complementary advances: (1) an improved regional quality-control strategy for preserving COSMIC-2 bending-angle data in cloudy regions, and (2) an assessment of the impact of assimilating Tianmu-1 RO observations from a newly deployed 23-satellite commercial constellation on the prediction of Typhoon Gaemi (2024).

First, we show that the widely used latitude-based quality control of COSMIC-2 bending-angle data leads to excessive removal of observations between 6–8 km near the Solomon Islands, where persistent summertime altostratus frequently reach above 6 km. Despite the long-wavelength nature of RO measurements—which makes them less sensitive to clouds—these regions were incorrectly flagged as outliers. By implementing a 2.5° × 2.5° local quality-control approach, the number of discarded observations in cloudy areas is substantially reduced, yielding a more spatially uniform deviation structure relative to the local mean. This regionally adaptive method better preserves high-quality RO data in both mid-tropospheric altostratus and lower-tropospheric Intertropical Convergence Zone environments.

Second, we evaluate the impact of assimilating over 30,000 daily RO profiles from the Tianmu-1 constellation using the GSI–WRF system. Assimilating Tianmu-1 data alone—without other satellite observations—reduces 120-hour track errors of Typhoon Gaemi by 20–40%, with the largest improvements beyond 48 hours. Diagnostics show that enhanced prediction skill arises mainly from improved inner-core temperature structure and better representation of the large-scale steering flow. Remarkably, the track forecasts with Tianmu-1 assimilation are even slightly better than the operational forecasts from the NCEP Global Forecast System (GFS).

Overall, these results highlight the increasing importance of high-density GNSS RO constellations in forecasting tropical cyclone intensity and track, and emphasize the value of cloud-aware, adaptive regional quality-control techniques in preserving cloud-affected observations. Future work will extend these adaptive quality-control strategies globally and examine synergistic assimilation of COSMIC-2, Tianmu-1, and other commercial RO datasets.