Do Solar Cycles Share Spectral Properties with Tropical Cyclones that Occur in the Western North Pacific Ocean?

Author:

Kim Ki-Beom,Kim Jung-Hee,Chang Heon-YoungORCID

Abstract

Understanding solar influences on extreme weather is important. Insight into the causes of extreme weather events, including the solar-terrestrial connection, would allow better preparation for these events and help minimize the damage caused by disasters that threaten the human population. In this study, we examined category three, four, and five tropical cyclones that occurred in the western North Pacific Ocean from 1977 to 2016. We compared long-term trends in the positions of tropical cyclone occurrence and development with variations of the observed sunspot area, the solar North-South asymmetry, and the southern oscillation index (SOI). We found that tropical cyclones formed, had their maximum intensity, and terminated more northward in latitude and more westward in longitude over the period analyzed; they also became stronger during that period. It was found that tropical cyclones cannot be correlated or anti-correlated with the solar cycle. No evidence showing that properties (including positions of occurrence/development and other characteristics) of tropical cyclones are modulated by solar activity was found, at least not in terms of a spectral analysis using the wavelet transform method.

Publisher

The Korean Space Science Society

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning-based Prediction of Sunspots using Fourier Transform Analysis of the Time Series;Publications of the Astronomical Society of the Pacific;2022-12-01

2. Active Days around Solar Minimum and Solar Cycle Parameter;Journal of Astronomy and Space Sciences;2021-03

3. Normalized Cross-Correlations of Solar Cycle and Physical Characteristics of Cloud;Journal of Astronomy and Space Sciences;2019-12

4. Association between Solar Variability and Teleconnection Index;Journal of Astronomy and Space Sciences;2019-09

5. Forecasting Sunspot Time Series Using Deep Learning Methods;Solar Physics;2019-05

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