20 Apr 2023
 | 20 Apr 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Evaluation of hygroscopic cloud seeding in warm rain process by a hybrid microphysics scheme on WRF model: a real case study

Kai-I Lin, Kao-Shen Chung, Sheng-Hsiang Wang, Li-Hsin Chen, Yu-Chieng Liou, Pay-Liam Lin, Wei-Yu Chang, Hsien-Jung Chiu, and Yi-Hui Chang

Abstract. To evaluate the hygroscopic cloud seeding in reality, this study develops a hybrid microphysics scheme on WRF model, WDM6–NCU, which involves 43 bins of seeded cloud condensation nuclei (CCN) in the WDM6 bulk method scheme. This scheme can describe the size distribution of seeded CCNs and explain the process of the CCN imbedding, cloud and raindrop formation in detail. Furthermore, based on the observational CCN size distribution applied in the modelling, a series of tests on cloud seeding was conducted during the seeding periods of 21–22 October, 2020 with stratocumulus clouds. The model simulation results reveal that seeding at in-cloud regions with an appropriate CCN size distribution can yield greater rainfall and that spreading the seeding agents over an area of 40–60 km2 is the most efficient strategy to create a sufficient precipitation rate. With regard to the microphysical processes, the main process that causes the enhancement of precipitation is the strengthening of the accretion process of raindrops. In addition, hygroscopic particles larger than 0.4 μm primarily contribute to cloud-seeding effects. The study results could be used as references for model development and warm cloud seeding operations.

Kai-I Lin et al.

Status: open (until 01 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-573', Anonymous Referee #1, 05 May 2023 reply
    • AC1: 'Reply on RC1', Kao-Shen Chung, 19 May 2023 reply

Kai-I Lin et al.

Kai-I Lin et al.


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Short summary
This study develops a hybrid microphysics scheme to enable the complex model simulation of cloud seeding based on the observational CCN size distribution. Our result illustrates more precipitation can be developed in the scenarios seeding at the in-cloud region and seeding over an area of tens km2 is the most efficient strategy, due to the strengthening of the accretion process. Moreover, particles bigger than 0.4 μm are the main factor contributing to cloud-seeding effects.