## 4 Assimilation and Impact of GNSS-R Data
### Impact of GNSS-R Data  
A number of Observing System Simulation Experiments (OSSEs) have been conducted to validate the impact of assimilating GNSS-R data on typhoon forecasting [86, 87, 88, 89]. OSSEs involve comparing the forecast outcomes with and without the assimilation of GNSS-R data. GNSS-R winds are simulated from a hurricane nature run (NR) that is realistic, with the error characteristics probably presenting in reality. This method helps determine the effectiveness of GNSS-R data in improving the accuracy and reliability of TC track and intensity predictions, thereby assessing its operational value in meteorological applications. [PERSON] assimilated CYGNSS wind speeds using the Gridpoint Statistical Interpolation 3-Dimensional Variational (GSI-3 DVAR) system, so that the TC structure, intensity, and position, together with large-scale variables, were all enhanced. The results highlighted the potential value of CYGNSS ocean surface wind speed data, and incorporating directional information can further enhance the accuracy of TC analyses and forecasts [89]. [PERSON] utilized the HWRF model and GSI system to evaluate the influence of assimilating CYGNSS ocean surface winds in a regional OSSE framework. The results revealed that adding GYGNSS data can actually improved the simulation's representation of the TC track and intensity, especially for the accuracy of ocean surface winds, inner-core structures, and surface fluxes forecasts. During the entire 72 h simulation, the minimum sea level pressure (MSLP) and surface maximum wind (MSW) errors had a 23% and 40% reduction, respectively, compared with the control experiment [88]. Figure 14 shows the time series of MSLP and MSW and also gives a track error bar of the experiments. [PERSON] assimilatedsimulated VAM-CYGNSS wind vectors using the GSI 3 DVAR system and HWRF model. The results indicated that the assimilation of VAM-CYGNSS wind vectors had reduced the MSW error by 2-5 kt (1 kt = 0.51 m/s) and reduced the MSLP error by 2-5 hPa [86]. The elevation in the intensity forecast was more significant than the reduction of the track error, and the improvement was also more consistent. [PERSON]'s experiments were similar to the above; the OSSE framework made use of the HWRF model and GSI system. To estimate the impact of assimilating CYGNSS winds and the difference in whether to add wind directions, CYGNSS winds were separated into scalar winds and wind vectors. In the simulation, the data assimilation cycles of 1, 3, and 6 h intervals were tested. At last, the 3 h interval assimilation was consistently best. In addition, CYGNSS scaler wind speeds improved the intensity forecasts in the first 48 h, resulting in a 2-5 hPa reduction for MSLP and a 2-6 kt elevation for MSW. As shown in Figure 15, the assimilation of CYGNSS wind vectors had a more significant effect on the 10 m wind field and structures in the HWRF model than assimilating wind speeds alone [87]. Figure 14: **(a,b)** are time series of MSLP and MSW, respectively, during the cycled DA period, 1200 UTC 1 to 0600 UTC 4 Aug 2005. Red for CTRL (assimilating simulated conventional observations), pink and blue for GYGNSS SUPO (CTRL plus CYGNSS speeds and thinning data to one per grid box, closest point kept) and THIN (SUPO but speeds averages kept), respectively, and black for NR. **(c–f)** As in **(a,b)**, but for track, track errors, MSLP, and MSW, respectively, between 0600 UTC 4 and 0000 UTC 6 Aug 2005. Different colored numbers mean the absolute error average of track and intensity in the whole analysis or simulation [88]. There are also studies that assimilate real CYGNSS wind speed observations in Observing System Experiments (OSEs). [PERSON] employed the WRF Data Assimilation (WRFDA) system, integrating 3D and 4D hybrid variational techniques, to assimilate CYGNSS wind speed during the Madden-Julian oscillation (MJO) event over the Indian Ocean. In Figures 16 and 17, the results indicated that CYGNSS data had a positive impact on wind fields [90].