摘要
基于WRFV3.6.1,利用其8个云微物理参数化方案对2010—2016年华南汛期(4—9月)的6个南风型暖区暴雨个例进行数值模拟与多方案集成试验,并采用基于对象的诊断评估方法(MODE)对模拟结果进行评估。结果发现对于大多数个例,WRF模式都能较好地模拟出暖区暴雨的降水带,对暖区降水带模拟最好的参数化方案是WSM6方案,其次是Lin方案;模拟效果较差的参数化方案为CAM5.1与NSSL 2-mon方案。选取模拟结果较好的个例进行诊断分析,发现不同参数化方案得到的动力学特征以及云微物理特征相关变量存在较大差异,导致模拟降水的差异。在单方案模拟的基础上,开展多方案集成试验,发现多方案集成方法能够有效降低模式模拟的不确定性,产生更稳定的模拟结果。
In this study,eight cloud microphysics parameterization schemes(Lin,WSM6,Thompson,Morrison 2-mon,CAM5.1,WDM5,WDM6 and NSSL 2-mon)in WRFV3.6.1 were applied to simulate six classical southerly types of warm-sector heavy rainfall(WSHR)during the rainy season over coastal Guangxi during the period of 2010-2016.The simulation results and ensemble mean(EMN)results are evaluated by the Method for Object-based Diagnostic Evaluation(MODE).In most cases,the WRF model was able to accurately simulate the rain belt of WSHR.The WSM6 scheme performed best on average,followed by the Lin scheme.In contrast,CAM5.1 and NSSL 2-mon were the schemes that exhibited the poorest simulations.It was also found that the dynamic and cloud microphysical characteristics of various schemes showed different performance.Based on the single scheme simulation results,the ensemble mean forecasting experiments were carried out according to the method of EMN,the results of which suggested that the EMN is able to reduce the forecast uncertainly of the model,and in turn produce more stable forecast results.
作者
智协飞
董甫
张玲
吉璐莹
朱寿鹏
ZHI Xiefei;DONG Fu;ZHANG Ling;JI Luying;ZHU Shoupeng(Key Laboratory of Meteorological Disasters,Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science and Technology,Nanjing 210044,China;WeatherOnline Institute of Meteorological Applications,Wuxi 214000,China)
出处
《大气科学学报》
CSCD
北大核心
2020年第5期867-879,共13页
Transactions of Atmospheric Sciences
基金
国家重点研发计划重点专项(2017YFC1502000)
江苏高校优势学科建设工程资助项目(PAPD)。
关键词
WRF模式
云微物理参数化方案
暖区暴雨
MODE
WRF model
cloud microphysics parameterization schemes
warm-sector heavy rainfall
method for object-based diagnostic evaluation