A study of the regularities in solar magnetic activity by singular spectral analysis

Loskutov, A. Yu.; Istomin, Igor; Kotlyarov, O. L.; Kuzanyan, K. M.

doi:10.1134/1.1415865

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…This is done by a principal component analysis of the covariance matrix r ik ¼ hR i R iþk i. SSA was first applied to the sunspot record by Rangarajan (1998) who only used this method for pre-filtering before the application of MEM. Loskutov et al (2001) who also give a good description of the method, already made a prediction for Cycle 24: a peak amplitude of 117 (v1 value).The forecast was later corrected slightly downwards to 106 (Kuzanyan et al 2008).…”

Section: Spectral Methodsmentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

194

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A review of solar cycle prediction methods and their performance is given, including early forecasts for Cycle 25. The review focuses on those aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is further restricted to the issue of predicting the amplitude (and optionally the epoch) of an upcoming solar maximum no later than right after the start of the given cycle. Prediction methods form three main groups. Precursor methods rely on the value of some measure of solar activity or magnetism at a specified time to predict the amplitude of the following solar maximum. The choice of a good precursor often implies considerable physical insight: indeed, it has become increasingly clear that the transition from purely empirical precursors to model-based methods is continuous. Model-based approaches can be further divided into two groups: predictions based on surface flux transport models and on consistent dynamo models. The implicit assumption of precursor methods is that each numbered solar cycle is a consistent unit in itself, while solar activity seems to consist of a series of much less tightly intercorrelated individual cycles. Extrapolation methods, in contrast, are based on the premise that the physical process giving rise to the sunspot number record is statistically homogeneous, i.e., the mathematical regularities underlying its variations are the same at any point of time, and therefore it lends itself to analysis and forecasting by time series methods. In their overall performance during the course of the last few solar cycles, precursor methods have clearly been superior to extrapolation methods. One method that has This article is a revised version of

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Section: Spectral Methodsmentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

194

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“…SSA was first applied to the sunspot record by Rangarajan (1998) who only used this method for pre-filtering before the application of MEM. Loskutov et al (2001) who also give a good description of the method, already made a prediction for cycle 24: a peak amplitude of 117. More recently, the forecast has been corrected slightly downwards to 106 (Kuzanyan et al, 2008).…”

Section: Spectral Methodsmentioning

confidence: 99%

Solar Cycle Prediction

Petrovay

2010

Living Rev. Solar Phys.

185

129

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A review of solar cycle prediction methods and their performance is given, including forecasts for cycle 24. The review focuses on those aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is further restricted to the issue of predicting the amplitude (and optionally the epoch) of an upcoming solar maximum no later than right after the start of the given cycle.Prediction methods form three main groups. Precursor methods rely on the value of some measure of solar activity or magnetism at a specified time to predict the amplitude of the following solar maximum. Their implicit assumption is that each numbered solar cycle is a consistent unit in itself, while solar activity seems to consist of a series of much less tightly intercorrelated individual cycles. Extrapolation methods, in contrast, are based on the premise that the physical process giving rise to the sunspot number record is statistically homogeneous, i.e., the mathematical regularities underlying its variations are the same at any point of time and, therefore, it lends itself to analysis and forecasting by time series methods. Finally, instead of an analysis of observational data alone, model based predictions use physically (more or less) consistent dynamo models in their attempts to predict solar activity.In their overall performance during the course of the last few solar cycles, precursor methods have clearly been superior to extrapolation methods. Nevertheless, most precursor methods overpredicted cycle 23, while some extrapolation methods may still be worth further study. Model based forecasts have not yet had a chance to prove their skills. One method that has yielded predictions consistently in the right range during the past few solar cycles is that of K. Schatten et al., whose approach is mainly based on the polar field precursor.The incipient cycle 24 will probably mark the end of the Modern Maximum, with the Sun switching to a state of less strong activity. It will therefore be an important testbed for cycle prediction methods and, by inference, for our understanding of the solar dynamo.

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“…However, according to Pesnell (2008), a summary of the predictions for the solar activity in cycle 24 made by more than 30 authors well before it started revealed that all these predictions anticipated a much stronger cycle 24 with a maximum sunspot number well above 100, approaching 185 (Thompson 1993) or 165 (Dikpati et al 2006) in some predictions, with most authors giving 130-140 (Hathaway & Wilson 2004;Kim et al 2006;Maris & Oncica 2006). These prediction methods included a number of disturbed days (Thompson 1993), linear regression analysis (Pesnell 2008), neural network forecast (Maris & Oncica 2006), a modified flux-transport dynamo model calibrated with historical sunspot data from middle-toequator latitudes (Dikpati et al 2006) or from the polar magnetic field data (Choudhuri et al 2007), and singular spectral analysis (Loskutov et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solar Activity From Solar Background Magnetic Field Variations in Cycles 21-23

Shepherd

Zharkov

Zharkova

2014

ApJ

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A comprehensive spectral analysis of both the solar background magnetic field (SBMF) in cycles 21-23 and the sunspot magnetic field in cycle 23 reported in our recent paper showed the presence of two principal components (PCs) of SBMF having opposite polarity, e.g., originating in the northern and southern hemispheres, respectively. Over a duration of one solar cycle, both waves are found to travel with an increasing phase shift toward the northern hemisphere in odd cycles 21 and 23 and to the southern hemisphere in even cycle 22. These waves were linked to solar dynamo waves assumed to form in different layers of the solar interior. In this paper, for the first time, the PCs of SBMF in cycles 21-23 are analyzed with the symbolic regression technique using Hamiltonian principles, allowing us to uncover the underlying mathematical laws governing these complex waves in the SBMF presented by PCs and to extrapolate these PCs to cycles 24-26. The PCs predicted for cycle 24 very closely fit (with an accuracy better than 98%) the PCs derived from the SBMF observations in this cycle. This approach also predicts a strong reduction of the SBMF in cycles 25 and 26 and, thus, a reduction of the resulting solar activity. This decrease is accompanied by an increasing phase shift between the two predicted PCs (magnetic waves) in cycle 25 leading to their full separation into the opposite hemispheres in cycle 26. The variations of the modulus summary of the two PCs in SBMF reveals a remarkable resemblance to the average number of sunspots in cycles 21-24 and to predictions of reduced sunspot numbers compared to cycle 24: 80% in cycle 25 and 40% in cycle 26.

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A study of the regularities in solar magnetic activity by singular spectral analysis

Cited by 10 publications

References 20 publications

Solar cycle prediction

Solar cycle prediction

Solar Cycle Prediction

Prediction of Solar Activity From Solar Background Magnetic Field Variations in Cycles 21-23

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