Jian et al. (2025) An advanced double-moment cloud microphysics scheme with explicit aerosol-cloud interactions and its performance in quantitative precipitation forecasting (QPF) in the CMA-MESO V5.0

Identification

• Journal: Atmospheric Research
• Year: 2025
• Date: 2025-11-22
• Authors: Bida Jian, Zhanshan Ma, Qi‐Jun Liu, Zhe Li, Liantang Deng, Haohao Nie, Chong Liu, Chuanfeng Zhao, Jian Sun, Xueshun Shen
• DOI: 10.1016/j.atmosres.2025.108649

Research Groups

• College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
• CMA Earth System Modeling and Prediction Centre, China Meteorological Administration (CMA), Beijing, China
• State Key Laboratory of Severe Weather, Chinese Academy of the Meteorological Sciences, Beijing, China
• Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
• Tianjin Weather Modification Office, Tianjin, China

Short Summary

This study develops an advanced double-moment cloud microphysics scheme with explicit aerosol-cloud interactions for the CMA-MESO V5.0 model, demonstrating its improved performance in quantitative precipitation forecasting, especially for extreme rainfall, under varying aerosol conditions.

Objective

• To develop and evaluate an advanced double-moment cloud microphysics scheme, explicitly incorporating aerosol-cloud interaction (ACI) parameterizations, within the CMA-MESO V5.0 regional model to investigate the effects of aerosols on cloud microphysical and precipitation processes and improve quantitative precipitation forecasting (QPF).

Study Configuration

• Spatial Scale: HuaBei region (for a severe convective precipitation case study), regional scale (CMA-MESO V5.0 model domain).
• Temporal Scale: A severe convective precipitation case (short-term event); batch simulations of 57 precipitation events from June to August 2018.

Methodology and Data

• Models used: CMA-MESO V5.0 regional model, advanced double-moment LiuMa cloud microphysics scheme (with explicit ACI parameterizations).
• Data sources: Model simulations (aerosol sensitivity experiments: clean, control, polluted scenarios; batch simulations of precipitation events).

Main Results

• Higher aerosol concentrations (polluted scenarios) enhance light rain and extreme heavy rain (>50 mm) but reduce moderate/heavy rain.
• Aerosols significantly modulate the spatio-temporal distribution of precipitation, with clean conditions showing greater variability in heavy precipitation compared to control.
• Microphysical budget analysis indicates that higher aerosol concentration increases cloud droplet concentration, suppresses warm rain processes (autoconversion, collision-coalescence), and reduces cloud water freezing and riming accretion efficiency in ice-phase processes, thereby weakening surface precipitation.
• The aerosol-aware LiuMa scheme improved threat scores (TS) and equitable threat scores (ETS) for extreme precipitation (>50 mm) forecasts in CMA-MESO, with ΔTS: +14.9 % and ΔETS: +15.6 % across 57 events.
• Under polluted conditions (19 events), the TS for extreme precipitation increased by 24.6 % and ETS by 26.0 %.

Contributions

• Development of an advanced double-moment cloud microphysics scheme with explicit ACI parameterizations for the CMA-MESO V5.0 model.
• Detailed investigation of aerosol impacts on cloud microphysical and precipitation processes across warm and cold phases through sensitivity experiments.
• Demonstration of the crucial role of ACI parameterization in enhancing the accuracy of heavy rainfall forecasts, particularly for extreme precipitation events, within a regional operational model.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Jian2025advanced,
  author = {Jian, Bida and Ma, Zhanshan and Liu, Qi‐Jun and Li, Zhe and Deng, Liantang and Nie, Haohao and Liu, Chong and Zhao, Chuanfeng and Sun, Jian and Shen, Xueshun},
  title = {An advanced double-moment cloud microphysics scheme with explicit aerosol-cloud interactions and its performance in quantitative precipitation forecasting (QPF) in the CMA-MESO V5.0},
  journal = {Atmospheric Research},
  year = {2025},
  doi = {10.1016/j.atmosres.2025.108649},
  url = {https://doi.org/10.1016/j.atmosres.2025.108649}
}