Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method

Abstract

The ridge line of the western Pacific subtropical high (WPSHRL) plays an important role in determining the shift in the summer rain belt in eastern China. In this study, we developed a forecast system for the June WPSHRL index based on the latest autumn and winter sea surface temperature (SST). Considering the adverse condition created by the small observed sample size, a very simple neural network (NN) model was selected to extract the non-linear relationship between input predictors (SST) and target predictands (WPSHRL) in the forecast system. In addition, some techniques were used to deal with the small sample size, enhance the stabilization of the forecast skills, and analyze the interpretability of the forecast system. The forecast experiments showed that the linear correlation coefficient between the predictions from the forecast system and their corresponding observations was around 0.6, and about three-fifths of the observed abnormal years (the years with an obviously high or low WPSHRL index) were successfully predicted. Furthermore, sensitivity experiments showed that the forecast system is relatively stable in terms of forecast skill. The above results suggest that the forecast system would be valuable in real-life applications.