Reconstruction of Dynamical Forecasting Model between Western Pacific Subtropical High Area Index and Its Summer Monsoon Impact Factors Based on the Improved Self-Memorization Principle
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With the objective of tackling the problem of inaccurate long-term western pacific subtropical high (WPSH) forecasts, based on the concept of dynamical model reconstruction and improved self-memorization principle, a new dynamical forecasting model of WPSH area (SI) index is developed. 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; the equation reconstruction by actual data is used as its dynamical core to overcome the problem of relatively simple dynamical core. The developed dynamical forecasting model of SI index is used to predict WPSH strength in the long term. Through 10 experiments of the WPSH abnormal years, forecast results within 25 days 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 satisfactory long-term forecasting results. In particular the aberrance of the subtropical high can be drawn and forecast. It is acknowledged that mechanism for the occurrence and development of WPSH is complex, so the discussion in this paper is therefore exploratory.
