摘要
利用中尺度数值预报模式WRF3.5,采用36、12和4 km三重嵌套,在积云参数化方案为BMJ条件下,选用WSM5、WSM6和Lin三种云微物理参数化方案,对发生在上海地区的两次典型特大暴雨(简称"0913"和"0825")进行模拟试验和对比分析,探讨不同云微物理参数化方案对上海暴雨模拟的影响。结果表明:三种方案总体上都较好地模拟出两次特大暴雨过程,但在降水落区、降水中心、降水强度等方面仍存在差异。再利用地面自动站、观测站的实测雨量以及自动站与CMORPH降水产品融合的逐时降水量网格数据,结合K指数、相对湿度、垂直速度和涡度散度等物理诊断量,从降水落区、降水中心和降水强度等方面对三种云微物理参数化方案的模拟结果进行对比分析。此外,通过对三种方案主要参数的比较以及三种方案模拟的冰、雪、霰粒子混合比的垂直廓线对相应的模拟结果进行解释。结果显示:WSM5微物理方案能更好地模拟出强降水的范围,其模拟的降水量较实测偏大;WSM6方案模拟的降水落区略有偏移,降水量偏小;Lin方案模拟的降水落区偏移较大。
In order to study the impact of physics parameterization schemes in the WRF model on rainstorm simulations, two typical heavy rainfall events （＂0825＂ and ＂0913＂） over Shanghai are simulated. The study was carried out with BMJ cumulus parameterization scheme and three physics parameterization schemes （Lin et al, WSM5 and WSM6 scheme） under three horizontal resolutions of 36 kin, 12 km and 4 Pan. The results indicated that all three physics schemes generally simulated the precipitation well, but they still differ in rainfall areas, rainfall center and intensity. By using the measured precipitation data of ground stations and the hourly rainfall grid data integrated from automatic stations and CMORPH, the paper analyzed the impact of different physics schemes on precipitation from the rainfall area, center and intensity, combined with K index, relative humidity, vertical velocity, vorticity, divergence and other physical diagnosis. Furthermore, the paper compared the difference among the three physics parameterization schemes, and explained the difference of the results by using the vertical profile of qsnow, qice and qgraupel. The results showed that the micro-physical WSM5 scheme can better simulate the rainfall area, but get heavier rain than the observation, and WSM6 can well simulate the rainfall intensity but drift a little in rainfall area, andthe simulated rainfall area of Lin has a drift.
作者
阚煜
刘朝顺
乔枫雪
束炯
刘延安
丁杨
KAN Yu LIU Chao-shun QIAO Feng-xue SHU Jiong LIU Yan-an DING Yang(Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University Shanghai 200241, China Joint Laboratory for Environmental Remote Sensing and Data Assimilation, ECNU ＆ CEODE, Shanghai 200241, China Institute of Climate Change, East China Normal University, Shanghai 200241, China Shanghai Meteorological Center, Shanghai 200030, China)
出处
《热带气象学报》
CSCD
北大核心
2017年第3期399-414,共16页
Journal of Tropical Meteorology
基金
上海市自然科学基金(17ZR1408600)
上海市科委重点支撑项目(13231203804)
国家自然科学基金(41271055
40801145)共同资助
