Variational Data Assimilation Experi

Variational Data Assimilation Experiments of Mei-Yu Front Rainstorms in China

The numerical forecasts of mei-yu front rainstorms in China has been an important issue. The intensity and pattern of the frontal rainfall are greatly influenced by the initial fields of the numerical model. The 4-dimensional variational data assimilation technology (4DVAR) can effectively assimilate all kinds of observed data, including rainfall data at the observed stations, so that the initial fields and the precipitation forecast can both be greatly improved. The non-hydrostatic meso-scale model (MM5) and its adjoint model are used to study the development of the mei-yu front rainstorm from 1200 UTC 25June to 0600 UTC 26 June 1999. By numerical simulation experiments and assimilation experiments, the T106 data and the observed 6-hour rainfall data are assimilated. The influences of many factors, such as the choice of the assimilated variables and the weighting coefficient, on the precipitation forecast results are studied. The numerical results show that 4DVAR is valuable and important to mei-yu front rainfall prediction.

作 者： 王云峰 王斌 韩月琪 朱民 侯志明 周毅 刘宇迪 寇正   作者单位： 王云峰(State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Institute of Meteorology, PLA University of Science and Technology, Nanjing 21)

王斌(State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029)

韩月琪,朱民,侯志明,周毅,刘宇迪,寇正(Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101) 

刊 名： 大气科学进展(英文版)  ISTIC SCI 英文刊名： ADVANCES IN ATMOSPHERIC SCIENCES  年，卷(期)： 2004 21(4)  分类号：   关键词： mei-yu front rainstorm   4DVAR   MM5 model and its adjoint model  

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