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

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500 hPa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．

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

Aiming at the difficulty of accurately constructing the dynamic model of subtropical high,based on the potential height field time series over 500 hPa layer of T106 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.