# FY3E GNOS II GNSS Reflectometry: Mission Review and First Results
## 4 Preliminary Validation Results  
Data from July 10 to October 31 were used for validation in this study. Each specular point was collocated with the wind speed from the ECMWF ERA5 reanalysis [23]. First, the GMFs v1.0 trained by the simulated data was validated by comparing the observables and collocated ECMWF wind speeds. Figure 12 shows the scatter density plots for GPS DDMA and GPS LES observables versus collocated ECMWF wind speeds. Figure 13 shows the corresponding plots for BDS observables. The GMF at an incidence angle of 30\({}^{\circ}\) for each observable is displayed in each figure as well. No obvious dependence on incidence angle was found in GMF v1.0. The results show that the GMFs trained from the simulated data match well with model winds, which validates the power calibration and retrieval algorithm. The development of new GMFs based on real measurements will be future work.  
Figure 11: GNOS II Level 2 MSS in 5 days (21–25 July 2021). The color grading represents the Level 2 MSS.  
Figure 10: GNOS II Level 2 wind speeds in 5 days (21–25 July 2021). High wind speeds from Typhoon “IN-FA” in the western Pacific Ocean can be observed (red circle).  
The L2 wind speeds are compared to the collocated ECMWF wind speeds herein. This paper mainly focuses on low to medium wind speeds (0-20 m/s), as most samples (>95%) of wind speed fall into this range and the ECMWF model winds are not accurate enough for high winds (>20 m/s). In the quality control (QC), data with range corrected gain (RCG) less than 15 and DDM SNR less than \(-\)3 dB were filtered out. The RCG was computed as in [2]. The SNR is computed as  
\[\mathrm{SNR}=\frac{P-N}{N} \tag{4}\]  
where \(P\) is the peak value of the DDM, and \(N\) is the noise floor of the DDM calculated by averaging the values of the first 20 rows before the specular point in the DDM where only noise exists. The thresholds in the QC were empirically selected to filter out data with low quality which corresponds to about 40% of the total population.  
After the quality control, the total numbers of wind speed observations were 3,313,036 for reflections from GPS (GPS-R) and 2,984,509 for reflections from BDS (BDS-R). Figure 14 shows the comparison between the L2 wind speeds and ECMWF wind speeds for data from GPS signals and BDS signals. The results show that most of the data are distributed evenly along the 1:1 diagonal. The root-mean-square error (RMSE) values for GPS-R and BDS-R are 1.78 and 1.70 m/s. The bias values are -0.06 and -0.40 m/s for GPS-R and BDS-R. The slight negative bias for BDS-R is possibly due to the inaccurate estimation of the GNSS-transmitted EIRP, since all other characteristics (e.g., signal structure and retrieval algorithm) of GPS-R and BDS-R are almost the same. The estimation of GNSS EIRP should be improved in future work.  
Figure 12: Scatter density plots for GPS DDMA and GPS LES observables versus collocated ECMWF wind speeds. The GMFs are displayed as black lines.  
Figure 13: Scatter density plots for BDS DDMA and BDS LES observables versus collocated ECMWF wind speeds. The GMFs are displayed as black lines.