A Dynamical–Statistical Forecasting Model of the Western Pacific Subtropical High Area Index Based on an Improved Self-Memorization Principle

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Abstract A new dynamical–statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization function was introduced to improve the traditional reconstruction model, thereby making it more effective for describing chaotic systems, such as WPSH. Processing actual data, the reconstruction equation was used as a dynamical core to overcome the problem of employing a simple core. The resulting dynamical–statistical forecasting model for AI was used to predict the strength of long-term WPSH forecasting. Based on 17 experiments with the WPSH during normal and abnormal years, forecast results for a period of 25 days were found to be good, with a correlation coefficient of ~0.80 and a mean absolute percentage error of <8%, showing that the improved model produced satisfactory long-term forecasting results. Additional experiments for predicting the ridgeline index (RI) and the west ridge-point index (WI) were also performed to demonstrate that the developed model was effective for the complete prediction of the WPSH. Compared with the authors’ previous models and other established models of reasonable complexity, the current model shows better long-term WPSH forecasting ability than do other models, meaning that the aberrations of the subtropical high could be defined and forecast by the model.