# Cloud Seeding Evidenced by Coherent Doppler Wind Lidar
## 5 Conclusions  
In this study, a method based on spectra analysis using a CDWL was developed for cloud-precipitation measurements, incorporating satellite and MWR. Multiple parameters derived from the lidar power spectrum provide potential evidence for the cloud- seeding effect. Transport of seeding particles in the ABL and cloud changes are characterized by CNR and velocity at the early stages of the seeding. Although the clouds were not penetrated completely, the two-layer structure of normalized skewness and spectrum width is detected in the layer warmer than 0 \({}^{\circ}\)C after seeding, which is similar to the melting layer structure. Multi-component spectra including cloud and precipitation are separated into four clusters, which have distinguishable distribution characteristics of spectrum width and vertical velocity. Clusters from the multi-component spectrum of cloud might be cloud water or melting ice, which is possibly related to a large amount of ice crystals produced  
Figure 9: Examples of cloud spectra from cloud middle to cloud base. **(a–c)** show cloud spectra of unseeded at T1 (5:17 p.m.); **(e–g)** show cloud spectra of after seeded at T2 (5:56 p.m.); **(i–k)** show cloud spectra at T3 (6:43 p.m.), when precipitation formed. **(d,h,l)** are aerosol or precipitation spectra below cloud base. Circles stand for the raw data of spectra. Black lines represent fitting results. The light purple shadow and pink shadow represent separated cloud droplet and cluster D, respectively. The yellow shadow and light blue shadow represent separated aerosol and rain components, respectively.  
by cloud seeding. Nevertheless, our findings provide evidence that CDWL has potential in monitoring the microphysical process of cloud-precipitation due to cloud seeding. With these multiple parameters retrieved from the lidar spectrum, the lidar can be extended to the research of melting layer characteristics, cloud phase identification, and classification, which is important for climate models, weather modification, and aviation safety. In future work, we will try to improve cloud penetration ability by increasing the power of the laser to study the cloud seeding process combined with other measurements. We also plan to integrate polarization detection into the lidar system [72].  
Conceptualization, H.X.; Methodology, J.Y. and K.W.; Software, J.Y.; Formal Analysis, H.X., J.Y. and K.W.; Investigation, J.Y.; Resources, J.Y.; Data Curation, J.Y.; Writing--Original Draft Preparation, J.Y. and K.W.; Writing--Review & Editing, T.W., L.W., Z.S. and Y.Y.; Visualization, J.Y.; Supervision, H.X. All authors have read and agreed to the published version of the manuscript.  
Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA22040601.  
Not applicable.  
The lidar data can be downloaded from [[https://figshare.com/articles/dataset/A_month_lidar_results_during_the_experiment/13232615/1](https://figshare.com/articles/dataset/A_month_lidar_results_during_the_experiment/13232615/1)]([https://figshare.com/articles/dataset/A_month_lidar_results_during_the_experiment/13232615/1](https://figshare.com/articles/dataset/A_month_lidar_results_during_the_experiment/13232615/1)), accessed on 18 September 2021. ([[https://doi.org/10.6084/m9.figshare.13232615.v1](https://doi.org/10.6084/m9.figshare.13232615.v1)]([https://doi.org/10.6084/m9.figshare.13232615.v1](https://doi.org/10.6084/m9.figshare.13232615.v1)), accessed on 18 September 2021).  
We would like to thank the National Satellite Meteorological Center of the China Meteorological Administration for providing support with the observational data.  
The authors declare no conflict of interest.