## 3 Assimilation and Impact of GNSS RO Data  
TCs are typically deep convections, where the height and temperature of the cloud tops are usually associated with the strength of the system [55; 56]. Currently, approximately 20,000 RO data points are assimilated globally every day [57], but it is also a challenge to obtain high vertical resolution and high-accuracy atmospheric temperature and water vapor profile observations near the tropopause, especially in severe weather conditions, such as TCs. In applied early operational forecasting of RO data, only data above an altitude of 4 km were used. Nowadays, most operational centers still do not assimilate RO data below the atmospheric ducting/trapping layer, which is characterized by large gradients in atmospheric refractivity and water vapor in the lower troposphere. However, lower-level RO observations, particularly in tropical regions, contain valuable information about atmospheric moisture, making their application extremely important. All-weather atmospheric information, especially near-surface data under cloudy or rainy conditions, is crucial for studying and validating theories on the genesis and evolution of TCs.  
In fact, RO data are highly accurate in the upper troposphere and lower stratosphere (UTLS), and many studies have utilized these observations to investigate deep convection in the UTLS, such as the structure of TCs, and cloud top heights [58]. [PERSON] et al. [59] determined cloud top heights using the peak of RO bending angle anomalies and validated their findings with data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and the International Satellite Cloud Climatology Project (ISCCP). They then utilized RO data to explore the vertical structure of TCs and analyzed the specific relationship between the cloud top height and TC intensity [60]. We can see the difference between the RO cloud top heights and the altitudes of the troposphere in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mission Name** & **Country** & **Task** & **Launch Time** \\ \hline FY-3C/D & China & GNSS RO & 2013 \\ TDS-1 & UK & GNSS-R & 2014 \\ CYGNSS & USA & GNSS-R & 2016 \\ PAZ & Spain & GNSS RO & 2018 \\ BF-1A/B & China & GNSS-R & 2019 \\ FSSCAT & Europe & GNSS-R & 2020 \\ FY-3E/F/G & China & GNSS RO/GNSS-R & 2021 \\ Spire & USA & GNSS RO/GNSS-R & 2021 \\ Tianmu-1 & China & GNSS RO/GNSS-R & 2023–2024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of spaceborne GNSS RO and GNSS-R missions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mission Name** & **Country** & **Task** & **Launch Time** \\ \hline GPS/MET & USA & GNSS RO & 1995 \\ Orsted & Danmark & GNSS RO & 1999 \\ Sunsat & South Africa & GNSS RO & 1999 \\ CHAMP & Germany & GNSS RO & 2000 \\ SAC-C & Argentina & GNSS RO & 2000 \\ GRACE & Germany & GNSS RO & 2002 \\ FedSat & Australia & GNSS RO & 2002 \\ UK-DMC & UK & GNSS-R & 2003 \\ COSMIC & USA/Chinese Taiwan & GNSS RO & 2006 \\ MetOp & Europe & GNSS RO & 2006 \\ TerraSAR-X & Germany & GNSS RO & 2007 \\ OCEANSAT-2 & Italy & GNSS RO & 2009 \\ TanDEM-X & Germany & GNSS RO & 2010 \\ YH-1/Phobos-Grunt & China/Russia & GNSS RO & 2011 \\ KOMPSAT & Korea & GNSS RO & 2013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)