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《Chinese Journal of Atmospheric Sciences》 2003-02
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Use of the Mesoscale Self-Memorization Model in the Heavy Rainfall Forecasting Experiments

Jia Xiaojing 1), Feng Guolin 2),and Cao Hongxing 3)1) (Chinese National Meteorological Center, Beijing 100081)2) (Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029)3) (Chinese Academy of Meteorological Sciences, Beijing 100081)  
In view of the fact that the atmospheric motion is an irreversible process, a memory function which can recall the observation data in the past has been introduced. Retrospective time integration scheme contains historical information so this scheme adapts to mesoscale weather forecast. The purpose of this paper is to apply this scheme to MM5 model and validate the efficiency of this scheme. Based on the atmospheric self-memorization principle, the retrospective time integration scheme in a mesoscale numerical model is developed which is called SMM5, and the experimental results are compared with the kernel model MM5. It shows that because of using information of several history fields, SMM5 can improve the prediction accuracy. As to the rainfall field, both the precipitation areas and precipitation intensities of SMM5 is more similar to the observed field than that of MM5.
【Fund】: 国家自然科学基金资助项目 4 0 2 750 31和 4 0 2 310 0 6;; 国家重点基础研究发展规划项目G19990 4 34 0 8共同
【CateGory Index】: P457
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