CCN数浓度及参数化方案对一次层云降水过程影响的数值研究  

Numerical research of CCN concentration and its parameterization scheme's influence on a stratiform precipitation process

作　　者：李艳伟[1] 全鑫 张泽锋[1] LI Yanwei, QUAN Xin, ZHANG Zefeng(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters （ CIC-FEMD ）Key Laboratory for Aerosol-Cloud- Precipitation of China Meteorological Administration,Nanjing University of Injormation Science ＆ Technology,Nanjing 210044, China)

机构地区：[1]南京信息工程大学气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室,江苏南京210044

出　　处：《大气科学学报》2018年第3期404-415,共12页Transactions of Atmospheric Sciences

基　　金：国家自然科学基金资助项目(41275152)

摘　　要：利用中尺度WRF模式(V3.7),采用WDM6双参数微物理方案,对2014年7月26日12时—28日06时发生在华东地区的一次层状云降水天气进行数值模拟。通过改变模式中初始云凝结核(CCN)数浓度及参数化方案,进行敏感试验,对模拟结果进行对比分析。改变CCN数浓度的结果表明,CCN数浓度对降水的影响复杂、非线性,随着CCN数浓度的增大,降水量减小。云水、霰混合比始终增加,雨水混合比表现为先增加后减小再增加的趋势,冰晶混合比则与之相反,呈现先减小再增加再减小的趋势,雪晶混合比呈现先减小后增加的趋势;改变CCN参数化方案的结果表明,两者模拟降水落区有差别,三参数方案更接近实际;降水产生后,三参数方案的CCN浓度一直高于双参数方案,且数值变化不大;双参数方案的结果显示暖云降水加强,冷云降水略弱,三参数方案则显示暖云降水较弱,冷云降水较强。Atmospheric aerosol possesses the characteristics of indirect radiative forcing,i.e.part of the particles in the aerosol are activated as CCN to form cloud droplets,which are then able to change the radiation characteristics of the cloud and affect the micro-physics in the cloud.The influence of CCN on cloud and precipitation is very complex.In order to study this process,we designed two sets of experiments.The WDM6 two-parameter microphysical scheme is incorporated into the mesoscale WRF（ V3. 7） to simulate a stratiform precipitation over the East China region from 12： 00 July 26,2014 to 6： 00 July 28,2014.By changing the number of initial cloud condensation nuclei（ CCN） in the model and parameterization scheme, sensitivity experiments were used to analyze the simulation results.The first experiment used double bi-directional nesting to perform the simulation.The initial concentration of CCN is based on the default of 108 kg-（-1） in the WRF model,which is roughly equivalent to 100 cm-（-3）, representing the CCN condition in a very clean area and performing the control test.The other representative values of CCN are 109 kg-（-1）,4×10-9 kg-（-1） and 10-（10） kg-（-1）, respectively corresponding to approximately 1 000 cm-（-3）,4 000 cm-（-3） and 10 000 cm-（-3）, representing the CCN concentrations in the cleaner,contaminated and heavily contaminated areas.The results show that the influence of CCN concentration on precipitation is complex and nonlinear.With the increase of the CCN concentration, the precipitation increases.The mixing ratio of cloud water and graupel always shows a rising trend; however, the mixing ratio of rainwater shows the tendency of first increasing, then decreasing,then increasing again.The mixing ratio of ice crystal,by contrast,f irst decreases, then increases, then decreases again.In addition, the mixing ratio of snow crystals first increases, then decreases.In addition to the above control and sensitivity experiments,another experiment was set up t

关 键 词：CCN 参数化方案 层状云降水 

分 类 号：P458.121[天文地球—大气科学及气象学]