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Ensemble Empirical Mode Decomposition Applied to Long-term Solar Time Series Analysis

机译:集成经验模式分解在长期太阳时间序列分析中的应用

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Solar time series manifests nonlinear and non-stationary behaviors, and perhaps multi-modal dynamical processes operating in solar magnetic indicators. In the present work, the novel ensemble empirical mode decomposition (EEMD) is applied to study the monthly distribution of sunspot areas produced by the extended time series of solar activity indices (ESAI) database in the time interval from 1821 January to 1989 December. It is established that the quasi-periodic variations of monthly sunspot areas consist of at least three well-defined dynamical components: one is the short-term variations which are obviously smaller than one year, the second one is the mid-term variations with periodic scales varying from 1 year to 15 years, and the last component is the periodic variation with periodicities larger than 15 years. The analysis results indicate the EEMD technique is an advanced tool for analyzing the weakly nonlinear and non-stationary dynamical behaviors of solar magnetic activity cycle.
机译:太阳时间序列表现出非线性和非平稳行为,并且可能在太阳磁指示器中运行着多模式动力学过程。在目前的工作中,采用新型的集合经验模式分解(EEMD)研究了太阳活动指数(ESAI)数据库的扩展时间序列在1月1821日至1989年12月之间产生的黑子区域的月度分布。已确定月黑子区域的准周期变化至少包括三个明确定义的动力学成分:一个是明显小于一年的短期变化,第二个是周期性的中期变化规模从1年到15年不等,最后一个组成部分是周期变化,周期大于15年。分析结果表明,EEMD技术是分析太阳磁活动周期的弱非线性和非平稳动力行为的先进工具。

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