Zhu et al. (2025) Ensemble forecast of precipitation enhancement potential using multiple microphysics parameterizations

Identification

• Journal: Atmospheric Research
• Year: 2025
• Date: 2025-11-20
• Authors: Jiangshan Zhu, Tiantian Wang, Hui He, Xiang’e Liu, Jiefan Yang, Tuanjie Hou, Zhaoxia Hu, Hengchi Lei
• DOI: 10.1016/j.atmosres.2025.108638

Research Groups

• State Key Laboratory of Atmospheric Environment and Extreme Meteorology (AEEM), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
• CMA Key Laboratory of Cloud-Precipitation Physics and Weather Modification (CPML), Beijing, China
• Weather Modification Centre, China Meteorological Administration, Beijing, China
• Beijing Weather Modification Centre, Beijing, China
• Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Short Summary

This study developed an 18-member ensemble forecast system within the WRF model to investigate the impact of microphysics schemes and initial/boundary conditions on uncertainties in cloud seeding simulations for precipitation enhancement. It found that both factors comparably influence supercooled liquid water and precipitation, with microphysics schemes having a greater impact on seeding-induced changes.

Objective

• To investigate the impact of microphysics schemes on uncertainties in cloud seeding modeling for precipitation enhancement potential.

Study Configuration

• Spatial Scale: Danjiangkou Reservoir catchment area in central China.
• Temporal Scale: A snowfall event involving post-frontal stratiform clouds.

Methodology and Data

• Models used: Weather Research and Forecasting (WRF) model, with a newly implemented standalone physics module for silver-iodide-related ice nucleation processes. An 18-member ensemble forecast was developed using six different bulk microphysics schemes.
• Data sources: Three sets of initial and lateral boundary conditions (IC/LBCs) were used to generate the ensemble members.

Main Results

• A new standalone physics module for silver-iodide-related ice nucleation processes was implemented in the WRF model, compatible with most bulk microphysics schemes.
• An 18-member cloud seeding ensemble forecast was developed using three sets of initial and lateral boundary conditions (IC/LBCs) and six microphysics schemes.
• The control ensemble (without cloud seeding) accurately reproduced the overall pattern of precipitation.
• The seeded ensemble predicted primarily positive precipitation enhancement potential over the target area.
• IC/LBCs and microphysics schemes exert comparable influences on variations in supercooled liquid water and precipitation.
• IC/LBCs had more pronounced effects on the spatial distribution of verified variables, while microphysics schemes had a greater influence on their intensity.
• Microphysics schemes exerted a greater influence on the variations of cloud seeding induced changes in microphysical variables and precipitation.

Contributions

• Development and implementation of a novel, compatible standalone physics module for silver-iodide-related ice nucleation processes within the WRF model.
• Establishment of an 18-member cloud seeding ensemble forecast system to quantify uncertainties in precipitation enhancement potential.
• Quantification of the comparable influences of initial/lateral boundary conditions and microphysics schemes on supercooled liquid water and precipitation, and their differential impacts on spatial distribution versus intensity.
• Highlighting the critical importance of incorporating multiple microphysics schemes in cloud seeding modeling for improved representation of uncertainties.

Funding

• Not specified in the provided text.

Citation

@article{Zhu2025Ensemble,
  author = {Zhu, Jiangshan and Wang, Tiantian and He, Hui and Liu, Xiang’e and Yang, Jiefan and Hou, Tuanjie and Hu, Zhaoxia and Lei, Hengchi},
  title = {Ensemble forecast of precipitation enhancement potential using multiple microphysics parameterizations},
  journal = {Atmospheric Research},
  year = {2025},
  doi = {10.1016/j.atmosres.2025.108638},
  url = {https://doi.org/10.1016/j.atmosres.2025.108638}
}