Case Study of the Influence of Cloud Droplet Spectral Dispersion on Cloud and Precipitation Simulations under Different Pollution Backgrounds

Cloud droplet spectral dispersion is essential to the parameterization of autoconversion, and it greatly influences surface precipitation. In this study, a model that combines the Weather Research and Forecast with chemistry (WRF-Chem) was used to simulate a precipitation process in the middle and lower reaches of the Yangtze River from January 3, 2019, to January 6, 2019. The microphysical changes in cloud and precipitation under clean and polluted backgrounds were studied using different values of cloud droplet spectral dispersion (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0). Our results show that the precipitation mainly originates from the autoconversion of cloud droplets to rain and the accretion of cloud droplets by rain. The accumulated precipitation under the clean conditions is larger than that under the polluted conditions because the clean conditions involve a smaller concentration of cloud droplets, which is beneficial for autoconversion and accretion. Although autoconversion and accretion are dominant during precipitation processes, the accumulated precipitation increases with an increase in the cloud droplet spectral dispersion. In addition, increased cloud droplet spectral dispersion increases the mass concentration of ice particles, resulting in an enhanced melting process and, consequently, increased raindrops. This further enhances surface precipitation. Overall, our results will improve the theoretical understanding of the response of the cloud and precipitation processes to aerosol and cloud droplet spectral dispersions.