## 3 Assimilation and Impact of GNSS RO Data
### Impact of GNSS RO Data  
Teng utilized COSMIC-2 data by assimilating GNSS RO data with the Weather Research and Forecasting (WRF) model, resulting in a false alarm ratio reduction by 20% and an accuracy rate enhancement by 19% [74], as shown in Table 2. The TC formation mentioned here is the prosperity of a tropical cloud cluster (TCC) into a TC. For each TCC, two experiments were conducted: GTS, with the assimilation of NCEP conventional observation data, and EPH, along with the conventional observations, there was the assimilation of a non-local excess phase operator [75] of GNSS RO data. [PERSON] [76] evaluated the impact of assimilating RO bending angles from COSMIC-2 on Hurricane Weather Research and Forecasting (HWRF) TC forecasts. The results showed that COSMIC-2 assimilation led to a significant 8-12% reduction, statistically, in the mean absolute error for the minimum central ocean level pressure in lead times of 36, 54, 60, and 108-120 h forecasts. Figures 8 and 9 illustrate that the area near the Central American landmass seemed to be the origin of low-to-mid-troposphere dry air intrusion, which invaded the Zeta circulation on the eastern side in the beginning 18 h of the free forecast. This dry air intrusion was more obvious in the C2 experiment, identical to the analysis differences. The difference between the C2 and Control experiment was the assimilation of COSMIC-2 bending angles. The Control assimilated all obtainable observations and excluded RO data, while the C2 involved the COSMIC-2 bending angles. [PERSON] [77] utilized simulated refractivity with conventional observations, such as radiance, temperature, and humidity, to perform a cycle forecast experiment and then assessed the potential impact on the TC track, maximum ocean surface wind speed, and integrated kinetic energy (IKE) forecasts. The results indicated that the global track forecasts have little relationship with GNSS RO data, but the wind speed and IKE forecasts had been actually slightly degraded at the lead time of 30 h to 60 h. [PERSON] [78] assimilated simulated low-latitude GPS RO refractivity using a mesoscale 4-dimension variational data assimilation system and found that RO data with robust characteristics and high accuracy are profitable for the TC forecast presented in Figures 10 and 11. Experiment MA_RO assimilated GPS RO data; additionally, it performed more specifically in TCs' structure and was more similar to the best track. [PERSON]'s study showed the impact of assimilating GNSS RO local refractivity and the bending angle on a typhoon that passed over Chinese Taiwan [79]. The forecast results at a 60-15 km resolution revealed that GNSS RO data actually improved the prediction of the TC track, as shown in Figure 12. Compared with refractivity, assimilation with bending angles had better performance, particularly for wind. In the experiments, NODA had no assimilation, GTS assimilated conventional data and satellite radiance without GNSS RO data, REF was GTS with GPS refractivity data, and BND was GTS with GPS bending angle data. For precipitation, we could see in Figure 13 that GNSS RO data helped a lot in finding the center location of the maximum rainfall but still had the potential to forecast the precipitation altitude. Summarizing the above cases, we also found that the forecasts at the initial time of mid-level moisture around the disturbance centers were increased after the assimilation of GNSS RO data. For developing cases, it also increased low-level vorticity, while for non-developing cases, it reduced vorticity throughout most of the troposphere. The current NCEP forecast system showed that the impact of COSMIC-2 RO data on NWP is significant in the upper troposphere and lower stratosphere [80], similar to analyses from the U.S. Navy system [81] and the ECMWF system. However, ECMWF also showed that GNSS RO data had improved the moisture and precipitation forecasts below a 5 km altitude [82], although there appeared to be considerable uncertainty in the troposphere below a 2 km altitude.