Articles | Volume 24, issue 23
https://doi.org/10.5194/acp-24-13833-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-13833-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Dec 2024
Research article |
| 13 Dec 2024
| 13 Dec 2024
Estimating the concentration of silver iodide needed to detect unambiguous signatures of glaciogenic cloud seeding
Jing Yang
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
CMA Key Laboratory of Cloud-Precipitation Physics and Weather Modification (CPML), Beijing, 100081, China
Jiaojiao Li
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Meilian Chen
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Xiaoqin Jing
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Bart Geerts
Department of Atmospheric Science, University of Wyoming, Laramie, WY 82071, USA
Zhien Wang
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794, USA
Yubao Liu
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Baojun Chen
CMA Key Laboratory of Cloud-Precipitation Physics and Weather Modification (CPML), Beijing, 100081, China
Shaofeng Hua
CMA Key Laboratory of Cloud-Precipitation Physics and Weather Modification (CPML), Beijing, 100081, China
Hao Hu
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Xiaobo Dong
Hebei Provincial Weather Modification Center, Shijiazhuang, 050021, China
Ping Tian
Beijing Weather Modification Center, Beijing, 100089, China
Qian Chen
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Yang Gao
CMA Key Laboratory of Cloud-Precipitation Physics and Weather Modification (CPML), Beijing, 100081, China
Data sets
Estimating AgI concentration Jing Yang https://doi.org/10.5281/zenodo.12798196
Model code and software
Weather Research & Forecasting Model (WRF) NCAR MMM https://www2.mmm.ucar.edu/wrf/users/download/get_source.html
Short summary
Detecting unambiguous signatures is vital for examining cloud-seeding impacts, but often, seeding signatures are immersed in natural variability. In this study, reflectivity changes induced by glaciogenic seeding using different AgI concentrations are investigated under various conditions, and a method is developed to estimate the AgI concentration needed to detect unambiguous seeding signatures. The results aid in operational seeding-based decision-making regarding the amount of AgI dispersed.
Detecting unambiguous signatures is vital for examining cloud-seeding impacts, but often,...
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