Abstract

Abstract. An airborne cloud seeding experiment was conducted over the eastern coast of Zhejiang, China, on 4 September 2016 during a major international event held in Hangzhou. In an attempt to reduce the likelihood of rainfall onset, a major airborne experiment for weather modification took place by seeding hygroscopic agents to warm clouds to reduce cloud droplet size. The effectiveness of seeding is examined, mainly for stratiform clouds with patchy small convective cells. A radar-domain-index (RDI) algorithm was proposed to analyze the seeding effect. The threshold strategy and the tracking radar echo by correlation (TREC) technique was applied in the domain selection. Factors analyzed include echo reflectivity parameters such as the mean and maximum echo intensity, the anomaly percentage of the grid number of effective echoes, the fractional contribution to the total reflectivities, and the vertically integrated liquid (VIL) water content during and after the seeding process. About 12 min after seeding ended, the composite reflectivity of seeded clouds decreased to a minimum (< 10 dBz) and the VIL of seeded clouds was ∼0.2 kg m−3. The echo top height dropped to ∼3.5 km, and the surface echoes were also weakened. By contrast, there was no significant variation in these echo parameters for the surrounding non-seeded clouds. The seeded cell appeared to have the shortest life cycle, as revealed by applying the cloud-cluster tracking method. The airborne Cloud Droplet Probe (CDP) measured cloud number concentration, effective diameter, and liquid water content, which gradually increased after the start of cloud seeding. This is probably caused by the hygroscopic growth of agent particles and collision–coalescence of small cloud droplets. However, these parameters sampled at ∼40 min after seeding decreased significantly, which is probably due to the excessive seeding agents generating a competition for cloud water and thus suppressing cloud development and precipitation. Overall, the physical phenomenon was captured in this study, but a more quantitative in-depth analysis of the underlying principle is needed.

Highlights

  • Weather modification, mainly by cloud seeding, is a common technique of changing the amount or intensity of precipitation

  • The seeding agents can serve as cloud condensation nuclei (CCN) to advance the collision–coalescence process in warm clouds (Jensen and Lee, 2008; Jung et al, 2015), or serve as ice nuclei (IN) to convert liquid water into ice crystals and strengthen vapor deposition, riming, and aggregation processes in super-cooled clouds

  • The goals of this study are to evaluate any consequence of aircraft hygroscopic seeding and to develop a feasible method for analyzing the cloud seeding effect for stratocumulus clouds by the following means: a. analyzing the variability of radar parameters in nearby regions with and without seeding b. tracking and comparing the lifetime between seeded and unseeded echoes c. examining the variation of surface precipitation d. analyzing the cloud microphysics before and after cloud seeding

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Summary

Introduction

Mainly by cloud seeding, is a common technique of changing the amount or intensity of precipitation. Relative to modeling and statistical evaluations, much fewer studies have been done to acquire direct observational evidence in field experiments on the effectiveness of cloud seeding (Kerr, 1982; Mather et al, 1997; Silverman, 2003). The increasing CCN from anthropogenic pollution causes higher cloud drop concentration and a narrower droplet spectrum, leading to suppressed drizzle formation and prolonged stratiform clouds (Bruintjes, 2003). They produce brighter clouds that are less efficient in precipitation (Albrecht, 1989). The goals of this study are to evaluate any consequence of aircraft hygroscopic seeding and to develop a feasible method for analyzing the cloud seeding effect for stratocumulus clouds by the following means: a. analyzing the variability of radar parameters in nearby regions with and without seeding b. tracking and comparing the lifetime between seeded and unseeded echoes c. examining the variation of surface precipitation d. analyzing the cloud microphysics before and after cloud seeding

Experimental and data description
Echo-cluster tracking and identification algorithm
Evaluation by RDI algorithm
Evaluation by the echo-cluster tracking and identification algorithm
Hourly variability of surface precipitation
Microphysical characteristics of the seeded cloud
Conclusions
Findings
Seeded

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