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利用自记忆模式试报我国汛期降水
作者姓名:Feng Guolin  Cao Hongxing  Gao Xinquan  Dong Wenjie  Chou Jifan
作者单位:Feng Guolin Mathematics and Physics College,Yangzhou University,Yangzhou 225009 Cao Hongxing Chinese Academy of Meteorological Science 46 Baishiqiaolu,Beijing 100081 Gao Xinquan Department of Meteorological Science,Lanzhou University,Lanzhou 730000
基金项目:The study was supported by the National Natural Science Foundation of China under Grant No.49875025 and National Key Program fo
摘    要:基于大气运动是一种不可逆过程的观点,引进了记忆过去时次资料的记忆函数。进一步阐明了大气运动的自记忆概念,导出了大气运动的自记忆性方程。基于自记忆原理,从大气热力学方程和水分平衡方程出发,建立了一个用于短期降水预报的数学模式。对6-8月我国汛期降水预测作了12年的回报试验,结果表明该模式的预测评分比目前的月预报有很大提高。

关 键 词:数值模式  气候预报  记忆

Prediction of Precipitation during Summer Monsoon with Self-memorial Model
Feng Guolin,Cao Hongxing,Gao Xinquan,Dong Wenjie,Chou Jifan.Prediction of Precipitation during Summer Monsoon with Self-memorial Model[J].Advances in Atmospheric Sciences,2001,18(5):701-709.
Authors:Feng Guolin  Cao Hongxing  Gao Xinquan  Dong Wenjie and Chou Jifan
Institution:Mathematics and Physics College, Yangzhou University, Yangzhou 225009,Chinese Academy of Meteorological Science 46 Baishiqiaolu, Beijing 100081,Department of Meteorological Science, Lanzhou University, Lanzhou 730000,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Department of Meteorological Science, Lanzhou University, Lanzhou 730000
Abstract: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 is introduced, moreover, a special concept of self-memorization of the atmospheric motion is proposed, and a so-called self-memorization equation of the atmospheric motion has been derived. Based on the self-memorization principle, a numerical model for decadal forecast is estab lished by means of the thermodynamic equation and the precipitation equation. The verification scores of the hindcasts of the model in the period from 1 to 12 years are much higher than that of monthly weather forecasts at present.
Keywords:Numerical model  Climatic prediction  Memory
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