Variational data assimilation experiments of mei-yu front rainstorms in China |
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| Authors: | WANG Yunfeng WANG Bin HAN Yueqi ZHU Min HOU Zhiming ZHOU Yi LIU Yudi KOU Zheng |
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| Institution: | 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,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 211101,Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101,Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101,Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101,Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101,Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101 |
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| Abstract: | 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. |
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| Keywords: | mei-yu front rainstorm 4DVAR MM5 model and its adjoint model |
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