Ensemble empirical mode decomposition applied to long-term solar time series analysis

Jianmei An; Yunfang Cai; Yi Qi; Xianping Wang; Yongyan Zuo

doi:10.1117/12.2502101

26 July 2018 Ensemble empirical mode decomposition applied to long-term solar time series analysis

Jianmei An, Yunfang Cai, Yi Qi, Xianping Wang, Yongyan Zuo

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 1082819 (2018) https://doi.org/10.1117/12.2502101
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

Abstract

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.

Citation Download Citation

Jianmei An, Yunfang Cai, Yi Qi, Xianping Wang, and Yongyan Zuo "Ensemble empirical mode decomposition applied to long-term solar time series analysis", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 1082819 (26 July 2018); https://doi.org/10.1117/12.2502101

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