## 3 Results and Discussion
### Contrasting Observations  
A small-range navigation monitoring test was carried out after the calibration test. In the actual operating conditions of CWML, a vehicle's traveling motion disturbs the conical scan, where the rotational and translational motion influence the pointing direction and measured radial velocities. Therefore, the CNS is equipped during the navigation monitoring test for the real-time correction of the errors caused by vehicle movement. As shown in Figure 14, the solid orange line represents the running trajectory of the vehicle-mounted platform, while the orange arrow represents its driving direction. Moreover, the direction of the wind profile arrow indicates the wind direction, and the arrow's color indicates the wind speed. During the contrasting observation experiment, the maximum distance between the two Lidars was less than 1.5 km, and the average distance between the two Lidars was about 600 m. The fixed Lidar was set up in a stand-still configuration on the roof of block C4 in the Innovation Industrial Park (117.12\({}^{\circ}\)E, 31.85\({}^{\circ}\)N) as a reference device. Contrast observations for both vehicle-mounted CWML and a reference fixed Lidar were carried out from 00:00 to 24:00 on 1 August to verify the wind field measurement ability of the vehicle-mounted Lidar. To demonstrate the performance and advantages of APFA and MC, we evaluated the following two criteria. The first was the root mean square of the difference (RMSD)  
Figure 14: Wind plume chart measured by the vehicle-mounted Lidar. Figure 13: Average speed and direction measured by the KC-WL-2006 Lidar and the reference Lidar. between the vehicle-mounted Lidar (\(V_{VM}\)) and the reference Lidar (\(V_{R}\)) to evaluate the biases of wind vector estimations:  
\[\text{RMSD}=\sqrt{\text{mean}(|V_{VM}-V_{R}|_{2})}\,, \tag{22}\]  
The second criterion was the proportion of good vector estimates (PGE), where the \(|V_{VM}-V_{R}|<\) 2.5 m/s was chosen as the threshold. As shown in Figure 15 \"Before APFA and MC\" refers to the processing results of the accumulated spectra mentioned in Section 2 without applying APFA and MC. Similarly, \"After APFA\" refers to the processing results after applying the APFA. \"After MC\" refers to the processing results after applying the MC. \"After APFA and MC\" indicates the processing results after applying APFA and MC. It can be seen from Figure 15a that the RMSDs of the estimated wind vector increased gradually with the increase in detection distance. The application of APFA plays a vital role in the wind vector estimation process, increasing the detection distance by about 500 m. The effective detection height of Lidar could reach 3 km. Furthermore, the vibration of the vehicle platform interferes with the cone scan, resulting in a broadening of the spectrum and confusion in the beam pointing. Here, the application of motion compensation dramatically improved the detection accuracy and detection distance. The case \"After APFA and MC\" achieved the best result, where the RMSD remains at a low level. Meanwhile, \"After APFA and MC\" is also the best for PGE (see Figure 15b). Notably, the PGE of the \"Before APFA and MC\" dropped to 90% at the 17 th range gate. Evidently, \"After APFA\" performed better than \"Before APFA and MC\", and its PGE dropped to 90% at the 23 rd range gate. In addition, the PGE of \"After MC\" method dropped to 90% at the 43 rd range gate. Thus, APFA and MC are proven to be effective for Lidar on a moving platform. As illustrated in Figure 16, the average wind speed and wind direction measured by the KC-WL-2006 Lidar and the vehicle-mounted Lidar exhibit good consistency. Furthermore, the correlation coefficients of the magnitude and direction of horizontal wind speed measured using two instruments were 0.94 and 0.91, respectively.