Evaluation of hygroscopic cloud seeding in warm-rain processes by a hybrid microphysics scheme using a Weather Research and Forecasting (WRF) model: a real case study

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

doi:https://doi.org/10.5194/acp-23-10423-2023

Articles | Volume 23, issue 18

https://doi.org/10.5194/acp-23-10423-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-10423-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 18

Research article

|

20 Sep 2023

Research article |

| 20 Sep 2023

Evaluation of hygroscopic cloud seeding in warm-rain processes by a hybrid microphysics scheme using a Weather Research and Forecasting (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

Data sets

Raw Data of RCWF and RCSL K.-I. Lin, K.-S. Chung, S.-H. Wang, L.-H. Chen, Y.-C. Liou, P.-L. Lin, W.-Y. Chang, H.-J. Chiu, and Y.-H. Chang https://doi.org/10.29840/DBAR.DB_RD_WFSL/Dataset

Short summary

This study develops a hybrid microphysics scheme to enable the complex model simulation of cloud seeding based on observational cloud condensation nuclei size distribution. Our results show that more precipitation can be developed in the scenarios seeding in 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.