# Cloud Seeding Evidenced by Coherent Doppler Wind Lidar
## 3 Verification Experiment  
The verification experiment was conducted at the campus of the University of Science and Technology of China, Hefei, China (117\({}^{\circ}\)15\({}^{\prime}\) E, 31\({}^{\circ}\)50\({}^{\prime}\) N). Measurements observed by the CDWL and MRR on 17 June 2019, 20 June 2019, and 12 July 2019 are shown in Figures 1 and 2, respectively. The CNR represents signal intensity and determines the accuracy of other retrieved results, as shown in Figure 1a-c. Clouds are characterized by a steep increase of carrier-to-noise ratio (CNR) at the cloud base, followed by a strong decrease of CNR at the cloud top. Spectrum broadening due to multiple scattering of clouds is observed, for instance, from 1:00 to 9:30 p.m., 20 June 2019 at an altitude of 0.5 km, as shown in Figure 1e. Spectrum broadening is obvious under precipitation for both aerosol and rain spectral components detected, as shown in Figure 1d-f. In addition, clear air turbulence, windshear, and aircraft wake lead to spectrum broadening due to strong velocity dispersion. Spectrum broadening due to multiple spectral components is more obvious than that of other factors, as statistically analyzed in [51]. The normalized skewness (\(Sk\)) is introduced to improve measurement of spectra shape, as in the following:  
\[Sk=(M_{r}-M_{l})/(M_{r}+M_{l}) \tag{1}\]  
Figure 1: Precipitation processes observed by the CDWL on 17 June 2019, 20 June 2019, and 12 July 2019. (**a–c**) CNR. (**d–f**) spectrum width. (**g–i**) skewness. (**j–l**) surface rain rate provided by a Davis weather station. Figure 2: Precipitation processes observed by the MRR on 17 June 2019, 20 June 2019, and 12 July 2019. (**a–c**), radar reflectivity. (**d–f**) vertical velocity. (**g–i**) rain rate. where  
\[M_{l}=\int_{i=m-1}^{m-N}P(f_{i})(f_{m}-f_{i})^{3}df,M_{r}=\int_{i=m+1}^{m+N}P(f_{i })(f_{i}-f_{m})^{3}df \tag{2}\]  
\(f_{i}\) and \(P(f_{i})\) are the Doppler frequency and power spectrum intensity at sampling point \(i\), respectively. \(f_{m}\) is associated with the maximum signal intensity. The calculation contains \(2N+1\) samples around \(f_{m}\), which are determined by the spectrum width and sample rate. The positive skewness means the spectrum is a right-tailed curve, and vice versa. Cubic spline interpolation can be pre-performed to smooth the spectrum curve and improve the robustness of the skewness calculation. The skewness deviates from 0 under precipitation, as shown in Figure 1g-i. Both spectrum width and skewness have vertical strip structures under the precipitation, similar to the MRR results shown in Figure 2. The spectrum skewness is more accurate to indicate precipitation with respect to the spectrum width. For instance, from 2:30 a.m. to 4:00 a.m. local time on 20 June 2019, the variation of spectrum width is not significant compared with that of skewness at an altitude of 0.1-0.5 km, while precipitation is detected by MRR. The precipitation time identified by the skewness is matched well with the real-time surface rain rate, as shown in Figure 1j-l. Precipitations at a high altitude of 0.5-2 km is detected by CDWL between 17 June 2019 and 12 July 2019. In these situations, the variation of skewness is significant at high altitudes, while little water is accumulated at the surface. Since the skewness is sensitive to the Doppler spectrum component, the CDWL can be used to detect precipitation and cloud structures accurately when compared with MRR. The CDWL can reflect cloud characteristics, while the detection range is affected by cloud attenuation. In order to illustrate the ability of the CDWL to identify spectral components, different types of Doppler spectra sampled from Figure 1 are shown in Figure 3. Figure 3a-c show the general spectrum in stable air conditions, the broadened spectrum due to the multiple scattering of clouds, and the Doppler spectrum with spectrum broadening due to turbulence at 12:00 p.m., respectively. Figure 3d-l shows different types of Doppler spectra during precipitation as identified by skewness.