一种基于改进自忆性原理的西太平洋副热带高压脊线指数动力-统计模型及其预报实验

A new dynamical forecasting model of Western Pacific subtropical high ridge line index based on the improved self-memorization principle and forecast experiments

针对西太平洋副热带高压中长期预报不准确的问题,基于动力系统反演思想和改进自忆性原理等途径建立了副高脊线指数的动力预报模型.本文创新性地引入了最大李雅普诺夫指数改进了传统的自忆性函数,使其对副热带高压之类的混沌非线性系统更加具有针对性,较好地克服了预报初值单一性问题;并根据实际观测资料重构的动力系统作为其动力核,克服了传统自忆性方程动力核设置较为简单的问题.用建立的副热带高压脊线指数动力预报模型实现了副高南北位置的中长期预报,通过了副高异常年份和正常年份的多次实验,可以发现模型在25天以内的预报效果很好,相关系数能达到0.80左右,相对误差控制在8%以下,证明了改进的模型具有较好的中长期预报效果.另外还将此模型推广到对副热带高压的面积指数和西脊点指数的预报,也取得了较好的预报效果,证明此方法适合于副热带高压的整体预报.鉴于西太副高发生发展机理的复杂性和预报的困难性,本文为副高等复杂天气系统的预报探索了新的方法思路.

Aiming at tackling the problem of inaccurate long-term Western Pacific Subtropical High（WPSH）forecasts,a new dynamical forecasting model of WPSH ridge line index（RI）is developed based on the concept of dynamical model reconstruction and improved self-memorization principle.To overcome the problem of single initial prediction value,the largest Lyapunov exponent is introduced to improve the traditional self-memorization function,making it more appropriate to describe the chaotic systems,such as WPSH;and the equation reconstructed by actual data is used as its dynamical core,getting rid of the problem existing in traditional equationthat dynamical core set is relatively simple.The developed dynamical forecasting model of RI index is used to predict WPSH strength in the long term.17 experiments of the WPSH abnormal years and normal years are performed,and forecast results within 25 months are found to be good,with a correlation coefficient of about 0.80 and root mean square error under 8%,showing that the improved model has yielded satisfactory long-term forecasting results.Additional experiments for predicting the area index（AI）and the west ridge point index（WI）are also performed to demonstrate that our method is effective for complete prediction of WPSH,especially the aberrance of the subtropical high can be drawn and forecast.The mechanism for the occurrence and development of WPSH is complex,and this paper offers a new thought for WPSH forecast research.