Reconstruction of statistical–dynamical model of the Western Pacific subtropical high and East Asian summer monsoon factors and its forecast experiments

Abnormal Western Pacific subtropical high (WPSH) activities often lead to extreme weather events in East Asia in some years. The relationship between the WPSH and the members of the East Asian summer monsoon (EASM) system is unknown, however. So the forecasting of abnormal WPSH activities is still difficult. 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 members of the EASM system on the WPSH anomalies. Based on the abnormal weather data from 2010, using the delay-relevant method and ANFIS, we conclude that the Mascarene cold high index, the Indian monsoon latent heat flux and the Tibetan high activity index can affect WPSH anomalies more significantly than other members of the EASM system. With the combination of genetic algorithms and statistical–dynamical reconstruction theory, a nonlinear statistical–dynamical model of the WPSH and three impact factors are objectively reconstructed from the actual data of 2010, and also, a dynamically extended forecasting experiment is carried out. To further test the forecasting performance of the reconstructed model, more experiments of nine WPSH abnormal years and eight WPSH normal years are also performed for comparison. All the results suggest that the forecasts of the subtropical high area index, the Mascarene cold high index, the Indian monsoon latent heat flux and the Tibetan high activity index all have good performance in the short and medium term (<25 days). Not only is the forecasting trend accurate, but also the root mean square error is no more than 8 %. Our paper not only provides 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.