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作 者:林文实[1] 孟金平[1,2] 蒙伟光[3] 樊琦[1] 李江南[1]
机构地区:[1]中山大学环境科学与工程学院,广州510275 [2]北京市气象局,北京100089 [3]中国气象局广州热带海洋气象研究所,广州510080
出 处:《气象科学》2009年第2期150-156,共7页Journal of the Meteorological Sciences
基 金:国家自然科学项目(40375036)
摘 要:用MM5模式模拟2004年12月20—23日发生在华北地区的一次暴雪天气的发生发展及其演变过程。在四个嵌套网格区域内分别采用Goddard方案(试验G)和Reisner 2方案(试验R)的两个纯显式冰相云微物理参数化方案进行试验。模拟结果表明:试验G和试验R均可以较好的模拟暴雪过程中的环流形势演变、降雪分布和强度,并且两个试验的结果差别不大,但是云中的微物理过程有很大的不同。试验G的主要云微物理过程包括云水的凝结增长、云冰的凝华增长、云冰初始化、云冰被雪碰并、云水被雪碰并、雪的凝华增长、云冰的Bergeron过程等;而试验R的主要云微物理过程包括云冰的凝华增长、云冰转化成雪、雪的凝华增长和霰的凝华增长等。In this paper, MM5 including several explicit resolvable-scale microphysical parameterizations was used to simulate a heavy snowstorm event that occurred in North China during 20-22 December 2004. Two experiments using the Goddard scheme ( experiment G) and Reinser scheme (experiment R) in four nested domains were conducted in order to test the sensitivity of different microphysical parameterization schemes to the simulations of snowfall. The primary results are as the follows: evolvement of circulation and snowfall in this snowstorm are simulated well in both experiment G and experiment R, and the results are similar in both of them. However, the characters of cloud microphysics are different from each other. Cloud ice mixing ratio in experiment G is more than that in experiment R. The dominant cloud microphysical processes in experiment G are condensation growth of cloud water, depositional growth of cloud ice, initiation (nucleation) of cloud ice, accretion of cloud ice by snow, accretion of cloud water by snow, depositional growth of snow, transfer of cloud ice to snow through Bergeron process and so on. The dominant cloud microphysical processes in experiment R are depositional growth of cloud ice, conversion of cloud ice to form snow, depositional growth of snow and depositional growth of graupel.
分 类 号:P435.1[天文地球—大气科学及气象学]
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