# Cloud Seeding Evidenced by Coherent Doppler Wind Lidar
## 4 Application in an Artificial Precipitation Operation
Figure 3:0 (**a**) The spectrum width of aerosol and cluster C; (**b**) horizontal speed of aerosol and cluster C; (**c**) horizontal direction of aerosol and cluster C; (**d**) vertical velocity of aerosol and cluster C; (**e**) the spectrum width of raindrop and cluster D; (**f**) horizontal speed of raindrop and cluster D; (**g**) horizontal rain direction of raindrop and cluster D; (**h**) vertical velocity of raindrop and cluster D.  
If it's drizzling below the cloud, it still has skewness increasing and spectrum width broadening, unless the weaker component is completely submerged by the stronger one. Generally, aerosol signals are dominant in the early stage of drizzling, and raindrops are dominant later as aerosols are washed. If the liquid layer is below the cloud, the power spectrum is symmetric with little skewness. A notable feature of the liquid water layer below the cloud is that the reflection signal intensity is significantly greater than that of drizzling. Clouds are usually extracted first by CNR, using the Haar wavelet covariance transform (HWCT) algorithm [51]. Examples of detected cloud spectra from cloud middle to cloud base are shown in Figure 3:1 Figure 9a-c show cloud spectra before cloud seeding at T1 (5:17 p.m.). That cloud is in liquid phase with skewness close to 0. In Figure 9e,f,i,j, cloud spectra extracted from the upper layer have largely normalized skewness and spectrum width, which indicates that multiple components exist. It is probable that cloud seeding generates a large number of ice crystals [70], resulting in big cloud droplet or that raindrops coexist below the melting layer [71]. Considering that cluster D falls slower than raindrops, it also could be that cloud seeding intensified the process of cloud-precipitation and generated graupel or small hail [59]. As the altitude continues to decrease, the reflection strength of cluster D decreases and cloud droplet dominates, as shown in Figure 9f,j. Only the cloud droplet exists at the cloud base with small normalized skewness and spectrum width, as shown in Figure 9g,k, which subsequently produces precipitation (Figure 9l). This change process is very similar to the melting layer detected by radar [30]. Applications of the lidar in cloud seeding are summarized into three aspects: (1) as an auxiliary measurement for cloud monitoring before operation., the lidar can well identify the cloud base and even penetrate the cloud when the cloud layer is thin. The cloud velocity provided by the lidar is useful for the preliminary preparation of cloud seeding; (2) providing windshear and turbulence alerts. The site is prone to windy weather, and the lidar plays an important role in ensuring safety of aircrafts during operation, especially in taking off and landing; and (3) tracking the cloud seeding, which is the focus of this paper. First, the transport of AgI leads to a significant increasing of CNR in the ABL after seeding, which proves that the detection range of lidar is within the seeding influence region. And changes in CNR and vertical velocity are detected at 4.0-4.5 km altitude. Similar to the linear polarization ratio related to the ratio of ice to water particle concentration, which is used to reflect ice-water balance due to seeding effects [26; 27], the CDWL spectrum also reflects the change of cloud phase to a certain extent. The spectrum width and skewness of cloud show a two-layer structure below 0 \({}^{\circ}\)C contour, which is similar to the melting layer structure. Combining characteristics of separation components, it could be inferred that seeding enhanced the development of the precipitation process, resulting in the detection of the two-layer structure at such a low altitude. Therefore, the CDWL  
Figure 3:2 The density distribution of the spectrum width and vertical velocity.