## 5 Conclusions  
In a horizontally homogeneous wind field, the vertical profiles of the horizontal wind vector can be obtained by sinusoidal fitting the radial wind speeds and azimuth angle in a VAD at each range gate. In this study, based on the radial wind speed data acquired by a scanning Doppler Lidar, an optimized method for raw radial wind speed data quality control and inversion result optimization is proposed (Figure 6). The method applies the \(\sigma_{CNR}\) filter to exclude data with exceptionally high or low CN\(R\) values, followed by an initial sinusoidal fitting. Then, data points with standardized residuals (\(|\mathcal{Z}_{e}|\)) exceeding 2 are dropped before a second sinusoidal fitting. Finally, only the inversion results with COF above 0.65 are retained. A comparison between the derived wind speed/wind direction from the optimized method and those from conventional observations underscore the advantages of the proposed method. A full vertical wind profile from near ground level to the height impacted by wind turbine blades can be obtained using the optimized method. The coefficient of determination (R\({}^{2}\)) between the wind speeds derived using the optimized method and those measured by the meteorological tower are higher compared to that of the traditional method (such as removing data with CN\(R<-27\) dB), and the root mean square error (RMSE) and mean absolute error (MAE) are lower than those of the traditional method. Specifically, for a 10 m wind speed, R\({}^{2}\) is improved from 0.826 to 0.932, and the MAE is reduced from 1.231 to 0.927; for a 10 m wind direction, R\({}^{2}\) is improved from 0.978 to 0.992, and the RMSE is decreased from 27.453 to 19.242, especially within the wind farm where the wind conditions are more complex. In this study, we improved the wind speed/wind direction inversion results by firstly removing outliers in the raw radial wind speed using filters such as \(\sigma_{CNR}\) and Z\({}_{e}\), then optimizing the derived wind speed/wind direction using the GOF threshold. The error of this method is notably lower than that of the traditional method, particularly during nighttime. Furthermore, this method significantly increases the amount of usable data,  
Figure 13: Data availability after every step of the quality control filter. (**a**) S3, (**b**) S1. The ratio of valid data to the total data amount in every 15\({}^{\circ}\) wind direction intervals at a radial distance of 450 m; the \(x\)-axis represents the radial distance of the Lidar’s VAD scans with a 10\({}^{\circ}\) elevation angle. (**c**) S3, (**d**) S1. The gray shaded areas indicate the azimuth range where the data availability of the optimized method is lower than that of the traditional methods. The \(y\)-axis represents the ratio of the amount of data in each wind direction interval to the total amount. especially on clear nights. The comparison of data availability between the traditional and optimized methods reveals that the optimized method proposed not only improves the accuracy of wind speed inversion but also increases the amount of usable data, especially in complex wind field environments. Low-elevation-angle VAD scanning has been proven to be a useful practice to obtain wind profiles below the minimum range of a vertically operating scanning Doppler Lidar. The optimized data quality control and inversion technique developed in this study is effective for obtaining wind profiles from near the ground to the height affected by wind turbines. This capability is vital for the in-depth analysis of the influence of wind turbine operation on a wind field and for the study of low-level jets, turbulence, and wind characteristics in wind farms and beyond. Future research should focus on validating and refining this method using data from diverse climatic conditions and terrains. Moreover, integrating different scanning strategies to determine an optimal balance between data quality and data availability is essential. Conceptualization Y.Y. ; writing--original draft preparation, T.M. and Y.Y.