# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 3 Results and Validation
### Wind Speed Retrieval Based on ERA5 and GDAS  
The atmospheric reanalysis numerical weather prediction (NWP) products from ECMWF ERA5 provided by the Climate Data Store (ADS) and the Global Data Assimilation System (GDAS) products provided by the Research Data Archive (RDA) were used as the ground truth wind speed in the GNSS-R wind speed retrieval. ERA5 is the fifth generation of NWP products provided by the European Center for Medium-range Weather Forecast (ECMWF), the spatial resolution of the grid wind field product is \(0.25^{\circ}\times 0.25^{\circ}\)and the time resolution reaches per hour [30]. National Centers for Environmental Prediction (NCEP) operates a global data assimilation system and the surface flux grid for the NCEP GDAS/FNL global surface flux products uses the T574 Gaussian global grid with a time resolution of 6 hours for wind speed products [31]. Both NWP products provide the U10 wind field. The wind speed data are interpolated bilinearly in space and linearly in time to matchup the DDM observables. In order to improve the accuracy of the reference wind speed in the training samples, samples with a deviation larger than 3 m/s from the two reference winds were removed. When the wind speed was less than 20 m/s, only the matched reference wind from ERA5 was used. When the wind speed was greater than 20 m/s, but less than 25 m/s, the average of two matching wind speeds was used. While the wind speed was greater than 25 m/s, only the GDAS wind was adopted. When DDMA and LES take the log scale, the fitting wind speed inversion model for DDMA and LES observables as shown in Figure 5, it is important to note that we have removed the parts of GMF that exceeded the range of the coordinate axis. Different levels of fold appeared where the wind speed was greater than 20 m/s, especially for the LES, which was obviously due to insufficient training samples. The correlation between the DDM observables and wind speed decreased as the wind speed increased, and when the wind speed was greater than 10 m/s, the first-order derivative change rate of the GMF function was small. This can be seen more clearly in Figure 6, which presents a group of DDMA and LES GMF at specific incidence angles. However, there is an apparent difference in the sensitivity between the two types of DDM wind speed observables, the LES could not respond to the variation of wind speed when it reached 25 m/s, while the DDMA was still sensitive to the change in the observables under the condition of strong wind speed. If the training sample is small or the DDM noise is large, the modeling error is easily shifted to GMF, which will definitely amplify the observation error in the final wind retrieval. However, the incident angle has little effect on the inversion model at low wind speed range, but as the wind speed increases, the influence becomes distinct. Figure 4: The distribution of daily specular point tracks of 8 CYGNSS satellites and buoys. When the NWP product was used as the surface truth wind, the density scatterplot of the retrieved wind speed against reference winds, residual versus reference winds, incident angle, and RCG is shown in Figure 7, where the blue dash line in Figure 7a represents 1:1, the wind speed deviation refers to the inverted wind speed subtracts the ground truth wind speed. The total number of matched test samples was 12,356,042 with bias and the Root Mean Square (RMS) of inferred winds are 0.14 m/s and 1.84 m/s, respectively. The inversed uncertainty of different wind intervals are also counted, and a wind speed greater than 10 m/s accounts for 6.71% with an uncertainty of 2.76 m/s; when the wind speed is larger than 15 m/s, the test samples account for 0.19% of the total training dataset with an uncertainty of 3.24 m/s, and a larger retrieval error appeared at higher wind speed range. The dependence of retrieval error on the NWP-derived ground truth wind is shown in Figure 7b, where positive biases appeared at reference wind speeds of 5-12 m/s, while negative biases can be seen at ground truth winds above 12 m/s.