Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 27 February 2022 / Approved: 2 March 2022 / Online: 2 March 2022 (07:41:45 CET)

A peer-reviewed article of this Preprint also exists.

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. Atmosphere 2022, 13, 660.

Journal reference: Atmosphere 2022, 13, 660
DOI: 10.3390/atmos13050660

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

EARTH SCIENCES, Atmospheric Science

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