Sunspot variability and an attempt to predict solar cycle 23 by adaptive filtering

Rangarajan, Govindan

doi:10.1186/bf03352090

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…Although the forecasting time may be taken larger than the sampling interval of the series (one day for the series studied in the present paper), our techniques aim at short-term rather than at long-term prediction, investigated with quite different techniques (see e.g. Rangarajan, 1998;Hathaway et al, 1999).…”

Section: Discussionmentioning

confidence: 99%

Solar variability: Evolution of correlation properties

Blanter

Shnirman

Mouël

2005

Journal of Atmospheric and Solar-Terrestrial Physics

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Section: Discussionmentioning

confidence: 99%

Solar variability: Evolution of correlation properties

Blanter

Shnirman

Mouël

2005

Journal of Atmospheric and Solar-Terrestrial Physics

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“…While in the methods discussed above the base was fixed (the trigonometric functions), SSA allows for the identification of a set of othogonal eigenfunctions that are most suitable for the problem. 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

195

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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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“…This is done by a principal component analysis of the covariance matrix r ik = R i R i+k . 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.…”

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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Sunspot variability and an attempt to predict solar cycle 23 by adaptive filtering

Cited by 13 publications

References 28 publications

Solar variability: Evolution of correlation properties

Solar variability: Evolution of correlation properties

Solar cycle prediction

Solar Cycle Prediction

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