# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 3 Results and Validation
### Wind Speed Retrieval Based on CCMP  
The cross-calibrated multi-platform ocean surface wind vector analysis product V2 (CCMP) combines cross-calibrated satellite microwave winds and in-situ wind, using a variational analysis method (VAM) to produce high-resolution (0.25\({}^{\circ}\)) gridded product with a time resolution of 6 h [32], it is also provided by RDA. Satellite-based passive and active microwave measurements mainly contribute to this wind product. The main reason for adopting the CCMP wind as ground truth wind speed is not only because it has high accuracy, but also because it is mainly based on a satellite-based microwave  
Figure 8: Root Mean Square and bias from Numerical Weather Prediction derived sea surface wind speed in different wind bins. Figure 7: Log(density) scatterplots of the retrieved wind speed with Numerical Weather Prediction ground truth wind speed (**a**), residual versus truth wind speed (**b**), incident angle (**c**), and range-corrected gain (**d**). scatterometer and radiometer. Using CCMP wind can further compare the accuracy of wind retrieval between GNSS-R and other satellite-based techniques. The CCMP data are also interpolated bilinearly in space and linearly in time to match the DDM observables for the training dataset. The empirical wind speed retrieval algorithm mentioned in Section 2.3 was used to construct the 2D GMF, Figure 9 shows the GMF established by DDMA and LES at an incident angle of 30\({}^{\circ}\). Magenta dots in the figure represent the empirical GMF directly obtained by weighting the training samples, and the black line represents the GMF obtained after parametric smoothing. It should be noted that the \(x\)-axis magnitude of the two subgraphs is different. Compared with DDMA, the distribution of the LES samples is closer to the \(y\)-axis. The comparison of the empirical GMF and parametric smoothed GMF shows that the parametric model of DDMA GMF was better than LES, and the latter had a slightly bigger gap at a lower wind speed range between the smoothed parametric GMF and original GMF, where the phenomenon indeed appeared at the full incidence angle dimension. We infer that the reason for this is that the number of samples used to construct the GMF was still quite small. Since the wind speed products provided by the CCMP dataset are only available until 30 December, 2017, the CCMP testing dataset only included 30 days of samples, and the matchup wind speed pairs reach a total of 10,272,765 groups. The density scatterplot of retrieved wind speed and reference winds, residual versus reference wind speed, incident angle, and RCG are shown in Figure 10 with the logarithmic in the number density of samples. Figure 10a shows that the highest density of samples occurred along the 1:1 blue dash line, and the statistic indicates that the bias and RMS of the CCMP-based inversed winds are 0.05 m/s and 1.68 m/s, respectively. In order to further clarify the inversion accuracy at the different wind speed ranges, the percent and uncertainty of the test samples were also presented, and the wind speed values in training dataset greater than 10 m/s account for 8.48% of the training set with an RMS 2.56 m/s, and wind speeds greater than 15 m/s account for 0.26% with 3.13 m/s. The behavior of the inversed residual with the CCMP wind, incidence angle, and RCG was similar to the NWP-based results, but the residual was closer to the zero error. Figure 9: Delay-Doppler Map Average (**a**) and Leading Edge Slope (**b**) geophysical model function at an incident angle of 30\({}^{\circ}\) with the CCMP wind. Figure 11 shows the mean bias and RMS of the CCMP-derived wind speed at different wind speed bins. The RMS uncertainty rose gradually with the wind speed below approximately 10 m/s, with bias around 0 m/s and RMS below 2 m/s. However, the inversion uncertainty increased sharply and is accompanied by fluctuations after the wind speed is above 15 m/s, the bias increasing is even surprising. However, the behavior of the RMS and bias is related to many factors.