Sun, C.; Shi, X.; Yan, H.; Jiang, Q.; Zeng, Y. Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method. Atmosphere2022, 13, 660.
Sun, C.; Shi, X.; Yan, H.; Jiang, Q.; Zeng, Y. Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method. Atmosphere 2022, 13, 660.
Sun, C.; Shi, X.; Yan, H.; Jiang, Q.; Zeng, Y. Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method. Atmosphere2022, 13, 660.
Sun, C.; Shi, X.; Yan, H.; Jiang, Q.; Zeng, Y. Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method. Atmosphere 2022, 13, 660.
Abstract
The ridge line of the western Pacific subtropical high (WPSHRL) plays an important role in determining the shift of 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 of 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 are used to deal with the adverse condition, enhance the stabilization of forecast skills, and analyze the interpretability of the forecast system. The forecast experiments show that the linear correlation coefficient between the predictions from the forecast system and their corresponding observations is around 0.6, and about three-fifths of the observed abnormal years (the years with an obviously high or low WPSHRL index) are successfully predicted. Furthermore, sensitivity experiments show that the forecast system is relatively stable in terms of forecast skill. The above evaluations suggest that the forecast system is valuable in a real application sense.
Keywords
machine learning; neural network; forecasting system; western Pacific subtropical high.
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
