## 4 Discussion  
The optimized quality control method proposed in this study will gradually reduce the amount of valid data as the control criterion becomes progressively stricter. Previous studies have shown that data availability can affect the climatic characteristics of the resulting wind field in a study area [40; 41]. For example, these studies shows that when the quality control process is more stringent, it will reduce the amount of usable data, which can lead to an overestimation of wind speed distribution. Therefore, it is necessary to study the impact of the optimized method on the amount of usable data. In Figure 13a,b, the amount of valid data at S3 and S1, after each step of control using the traditional and optimized methods are given, with the horizontal axis indicating the radial distance for the 10\({}^{\circ}\) elevation angle VAD. Within the wind farm (S3) (Figure 13a), the amount of available data (data availability) after \(\sigma_{CNR}\) filtering is higher than the traditional method at all heights. After \(Z_{e}\) filtering and GOF optimization, the amount of available data is higher than the traditional method at radial distances below 400 m and above 1750 m, while it is similar to that of the traditional method in the range of 400-1750 m. It is noted that at the radial distance of 450 m, the amount of available data is significantly lower than the traditional method, which is attributable to the presence of wind turbines in the azimuth range of 225\({}^{\circ}\)-15\({}^{\circ}\) at S3, with the vertical height corresponding to this radial distance close to the hub height of the wind turbines. Upwind of the wind farm (S1) (Figure 13b), the data availability after \(\sigma_{CNR}\) filtering is similar to the traditional method but slightly less than the traditional method after \(Z_{e}\) filtering and GOF optimization (about 5%). The above analysis suggests that the quality control of Lidar radial wind speed data and the optimized processing procedure proposed in this study can retain more valid data in complex wind field environments, such as inside a wind farm. Figure 13c,d present the ratio of the available data in each 15\({}^{\circ}\) wind direction interval to the total data at S3 and S1, using the traditional and the optimized methods, respectively. At S1, the data availability of the traditional method and the optimized method are comparable in all azimuths (Figure 13d), while the data availability of the optimized method is lower than that of the traditional method in the range of 210\({}^{\circ}\)-42\({}^{\circ}\) at S3 (Figure 13c) but still exceeds 75% (Figure 13a), which indicates that the optimized method can effectively filter out the data affected by wind turbines. Overall, the optimized method not only improves the accuracy of wind speed inversion but also maintains a larger amount of valid data than the traditional method, particularly in complex wind field environments.  
Figure 12: Comparison of statistical measures between wind speeds derived by the traditional method and the optimized method and wind speeds reconstructed by DBS at S3: (**a**) MAE; (**b**) RMSE; (**c**) R\({}^{2}\). The \(x\)-axis represents the vertical height, and the \(y\)-axis represents the values of each statistical measure.