Impacts of GNSS RO Data on Typhoon Forecasts Using Global FV3GFS with GSI 4DEnVar

Abstract

The FORMOSAT-7/COSMIC-2 satellites were launched in 2019, which can provide considerably larger amounts of radio occultation (RO) observations than the FORMOSAT-3/COSMIC satellites. The radio signals emitted from the global navigation satellites system (GNSS) are received by these low Earth orbit (LEO) satellites to provide the so-called bending angle accounting for bending of the rays after penetrating through the atmosphere. Deeper RO observations can be retrieved from FORMOSAT-7/COSMIC-2 for use in RO data assimilation to improve forecasts of tropical cyclones. This study used the global model FV3GFS with the finest grid resolution of about 25 km to simulate five selected typhoons over the western North Pacific, including Hagibis in 2019, Maysak and Haishen in 2020, and Kompasu and Rai in 2021. For each case, two experiments were conducted with and without assimilating FORMOSAT-7/COSMIC-2 RO bending angle. The RO data were assimilated by the GSI 4DEnVar data assimilation system for a total period of 4 days (with 6 h assimilation window) before the typhoon genesis time, followed by a forecast length of 120 h. The RO data assimilation improved the typhoon track forecasts on average of 42 runs. However, no significantly positive impacts, in general, were found on the typhoon intensity forecasts, except for Maysak. Analyses for Maysak attributed the improved intensity forecast mainly to the improved analyses for wind, temperature, and moisture in the mid-upper troposphere after data assimilation. Consequently, the RO data largely enhanced the evolving intensity of the typhoon at a more consistent movement as explained by the wavenumber-one vorticity budget analysis. On the other hand, a noted improvement on the wind analysis, but still with degraded temperature analysis above the boundary layer, also improved track forecast at some specific times for Hagibis. The predictability of typhoon track and intensity as marginally improved by use of the large RO data remains very challenging to be well explored.