经验正交函数与遗传算法结合的副热带高压位势场非线性模型反演  

Non-Linear Dynamic Model Retrieval of Subtropical High Based on Empirical Orthogonal Function and Genetic Algorithm

作　　者：张韧[1,2,3] 洪梅[1] 孙照渤[2] 牛生杰[2] 朱伟军[2] 闵锦忠[2] 万齐林[3] 林建忠（推荐） 

机构地区：[1]解放军理工大学气象学院海洋与空间环境系,南京211101 [2]南京信息工程大学,江苏省气象灾害重点实验室,南京210044 [3]中国气象局热带海洋气象研究所,广州510080 [4]不详

出　　处：《应用数学和力学》2006年第12期1439-1446,共8页Applied Mathematics and Mechanics

基　　金：国家自然科学基金资助项目(40375019);热带海洋气象科学研究基金资助项目(200609);江苏省气象灾害重点实验室开放课题资助项目(KLME0507)

摘　　要：针对副热带高压的动力预报模型难以准确构建的困难,基于T106数值预报产品500hPa位势高度场序列,用经验正交函数(EOF)分解方法对位势场序列进行了时、空分解,引入了动力系统重构思想,以EOF分解的空间模态的时间系数序列作为动力模型变量,用遗传算法全局搜索和并行计算优势,进行了动力模型参数的优化反演,建立了客观合理的非线性动力模型·通过对动力模型积分和EOF的时、空重构,实现了副热带高压的中、长期预报·试验结果表明,本文反演的动力模型的副热带高压预报效果优于常规的数值预报产品,该研究工作为副热带高压等复杂天气系统和要素场预报提供了新的方法思路和技术途径·Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field lime series over 500 hPa layer of TI06 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.

关 键 词：遗传算法 经验正交函数 非线性模型反演 副热带高压 

分 类 号：O175.14[理学—数学] P433[理学—基础数学]