基于模糊系统的西太平洋副热带高压异常年份的影响因子检测分析和动力预报模型反演

Analysis of the impact factors of abnormal Western Pacific subtropical high years based on the fuzzy systems and the dynamical forecast model inversion

基于自适应网络模糊推理系统(ANFIS),讨论了滤除主信号之上影响因子噪声音的方法和途径,针对2010年的副热带高压异常的天气事实,首先用时滞相关法从观测资料中分析出2010年夏季风系统主要成员中对副热带高压异常影响最显著的3个因子:马斯克林高压、索马里低空急流和印度季风潜热通量,并用ANFIS诊断检测了这3个因子对副热带高压异常的影响和贡献。由于ANFIS系统具有非线性、容错性和自适应学习等特性,因此适宜于研究和模拟副热带高压等动力学不易准确描述的问题。在此诊断分析基础上,采用遗传算法和动力重构理论相结合的技术路径,从2010年实际的观测资料序列中客观准确地反演重构出非线性的副热带高压及其影响因子的动力模型,并对其进行了动力延伸预报试验。多次试验结果表明,预报的副热带高压面积指数、马斯克林冷高压强度指数、索马里低空急流和印度潜热通量指数在25天以内的中短期预报效果较好,误差不超过10%,并且预报出了指数的变化趋势。为西太平洋副热带高压与东亚夏季风系统的关联性和西太平洋副热带高压指数预测研究提供了一种新的思路和方法。

Based on the adaptive network fuzzy inference system （ANFIS）, the ways and methods to filter out noise of the im- pact factors from the main signal are discussed. Aiming at the abnormal weather events in 2010, with the delay-relevant meth- od, three members of the summer monsoon system which most significantly affected the subtropical high anomalies in 2010 are analyzed from the observational data. They are the Mascarene cold high index, Somali low-level jet and the Indian monsoon la- tent heat flux. Because of adaptive learning and nonlinear superiority of the adaptive-network-based fuzzy inference system （ANFIS）, it can be used to analyze and detect the influence and contribution of the members of the EASM （East Asia Summer Monsoon） system on the Western Pacific subtropical high （WPSH） anomalies. With the combination of the genetic algorithms and the statistical-dynamical reconstruction theory, a nonlinear statistical-dynamical model of the WPSH and three impact fac- tors are objectively reconstructed from the actual data of 2010, and also a dynamically extended forecasting experiment is car- ried out. The results show that the forecasts of the subtropical high area index, the Mascarene cold high index, the Somali low- level jet and the Indian monsoon latent heat flux all have good performance in the short and medium ranges （less than 25 d）. Not only is the forecasting trend accurate, but also the root mean square error is no more than 10%. Our paper not only pro- vides new thinking for research on the association between the WPSH and EASM system, but also provides a new method for the prediction of the WPSH area index.