Applying the WRF Double-Moment Six-Class Microphysics Scheme in the GRAPES_Meso Model： A Case Study

作     者：Meng ZHANG Hong WANG Xiaoye ZHANG Yue PENG Huizheng CHE 

作者机构：Institute of Urban Meteorology China Meteorological Administration Beijing 100089 State Key Laboratory of Severe Weather/Institute of Atmospheric Composition Chinese Academy of Meteorological Sciences Beijing 100081 Center for Excellence in Regional Atmospheric Environment Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration Nanjing University of Information Science & Technology Nanjing 210044 Beijing Meteorological Service Center Beijing 100089 

出 版 物：《Journal of Meteorological Research》 (气象学报（英文版）)

年 卷 期：2018年第32卷第2期

页      面：246-264页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：Supported by the National Key Project(2016YFC0203306) National Natural Science Foundation of China(41590874) National(Key)973 Program(2014CB441201) Chinese Academy of Meteorological Sciences’ Project(2017Z001) Key Project of Air Pollution Cause and Control(DQGG0104) 

主　　题：mesoscale version of the Global/Regional Assimilation and Prediction System WRF single-moment 6-class scheme microphysics scheme double moment cloud condensation nuclei 

摘      要：This study incorporated the Weather Research and Forecasting（WRF） model double-moment 6-class（WDM6） microphysics scheme into the mesoscale version of the Global/Regional Assimilation and Pr Ediction System（GRAPES_Meso）. A rainfall event that occurred during 3–5 June 2015 around Beijing was simulated by using the WDM6, the WRF single-moment 6-class scheme（WSM6）, and the NCEP 5-class scheme, respectively. The results show that both the distribution and magnitude of the rainfall simulated with WDM6 were more consistent with the observation. Compared with WDM6, WSM6 simulated larger cloud liquid water content, which provided more water vapor for graupel growth, leading to increased precipitation in the cold-rain processes. For areas with the warmrain processes, the sensitivity experiments using WDM6 showed that an increase in cloud condensation nuclei（CCN）number concentration led to enhanced CCN activation ratio and larger cloud droplet number concentration（Nc） but decreased cloud droplet effective diameter. The formation of more small-size cloud droplets resulted in a decrease in raindrop number concentration（Nr）, inhibiting the warm-rain processes, thus gradually decreasing the amount of precipitation. For areas mainly with the cold-rain processes, the overall amount of precipitation increased; however, it gradually decreased when the CCN number concentration reached a certain magnitude. Hence, the effect of CCN number concentration on precipitation exhibits significant differences in different rainfall areas of the same precipitation event.