A follow-on mission of FS3/C is FORMOSAT-7/COSMIC-2 (hereafter FS7/C2), which provides abundant RO soundings over the \(\pm 50^{\circ}\) latitudes [16]. The accuracy and resolution specifications for FORMOSAT-7/COSMIC-2 are discussed by [PERSON] et al. [17] and [PERSON] et al. [18]. Assimilation of RO data from FS3/C has been shown in previous studies to improve the initial temperature and moisture analysis for TCs and their surrounding environments, and then improve the regional and global model forecasts (e.g., [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]). The impacts of RO data have been illustrated mostly for track forecasts, while providing less noted improvement in intensity forecasts [22; 23]. Recovery of initial more intense TCs, even after the RO data assimilation has been performed, may be beneficial for improving the TC intensity forecasts when considering a merit of DVI that can spin up the initial TCs. The GSI was previously applied to assimilate RO data in combination with the CWB global forecasting system model for the cycling DA, and then produced subsequent \"free forecasts\" with the MPAS simulation [22]. The process can be regarded as a one-way MPAS-GSI system, which uses the MPAS simulation only in the free forecasting. In the DA, the CWB global model is employed in the 3 DVAR/EnKF analysis for assimilating most operationally available observations. The RO data have illustrated the positive impacts on track forecasts of Typhoon Nepartak (2016) and Megi (2016) in [22]. In this study, we aim to further investigate the RO data impact for Nepartak (2016) with an alternative assimilation/model system where the same multiple-resolution global model is utilized in the hybrid cycling DA as well as the free forecast. This MPAS-GSI is considered as a two-way system, which is an option of global DA with various satellite radiances alternative to MPAS-DART developed by [PERSON] et al. [32]. The newly developed DVI [13] is also integrated into the assimilation system to explore the performance of the additional DVI on the track and intensity forecasts. The cycled model integration in the DVI as described later may provide a more consistent and stronger initial typhoon vortex that is often less spin up by RO data and even satellite radiance assimilation. For RO data assimilation, we will also compare the performance differences between the assimilation with local bending angle and refractivity operators, both built in GSI, and their impacts on the ensuing forecasts. The impacts of DVI after RO data assimilation are investigated to exhibit the relative importance of vortex initialization and RO data assimilation for the typhoon forecasts at high resolution in this study. The numerical methods, including the global model, data assimilation, RO observation operators and the DVI scheme, and the experiments conducted in this study, are introduced in Section 2. The initial analysis, forecast results and some discussions for Typhoon Nepartak (2016) are provided in Section 3. Additionally, the impact study is extended to Typhoon Mitag (2019) with the FS7/C2 RO data assimilation that will be discussed in this section. Finally, conclusions are given in Section 4.