The solar cycle as a forced and damped harmonic oscillator: long-term variations of the amplitudes, frequencies and phases

Hiremath, K. M.

doi:10.1051/0004-6361:20042619

Cited by 19 publications

(20 citation statements)

References 21 publications

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“…Damping would cause the system to relax to the driving period 2p=x 0 if there were no stochastic disturbances to this equilibrium. Hiremath (2006) fitted the parameters of the forced and damped oscillator model to each observed solar cycle individually; then in a later work (Hiremath 2008) he applied linear regression to the resulting series to provide a forecast (see Sect. 4.1 above).…”

Section: The Sun As An Oscillatormentioning

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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Section: The Sun As An Oscillatormentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

195

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“…They used a wavelet analysis on geomagnetic index (aa) and the yearly averaged Wolf sunspot numbers since 1844 and found that, a non-linear coupling function between sunspot maxima and aa minima modulations. Hiremath (2006) modeled the solar cycle as a forced and damped harmonic oscillator and from all the 22 cycles , long-term amplitudes, frequencies, phases and decay factor are obtained. Using these physical parameters of the previous 22 solar cycles and by an autoregressive model, they predict the amplitude and period of the future 15 solar cycles.…”

Section: Sunspot Cycle Prediction Techniques -A Reviewmentioning

confidence: 99%

Sunspot cycle prediction using multivariate regression and binary mixture of Laplace distribution model

Sabarinath

Anilkumar

2018

J Earth Syst Sci

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Prediction of sunspot cycle is a vital activity in space mission planning and various engineering decision making. In the present study, the sunspot cycle prediction has been carried out by a hybrid model which employs multivariate regression technique and the binary mixture of Laplace distribution (BMLD) function. The Expectation Maximization (EM) algorithm is being applied to the multivariate regression analysis to obtain a robust prediction of the sunspot cycle. Sunspot cycle 24 has been predicted using this technique. Multivariate regression model has been derived based on the available cycles 1 to 23. This model could predict cycle 24 as an average of previous cycles. Prediction from this model has been refined to capture the cycle characteristics such as bimodal peak at the high solar activity period by incorporating a predicted peak sunspot number from the BMLD model. This revised prediction has shown more accuracy in forecasting the major discrete features of sunspot cycle like maximum amplitude, the Gnevyshev gap, time duration from peak to peak amplitude, and the epoch of peak amplitude. This refined prediction shows that cycle 24 will be having a peak amplitude of 78 with an uncertainty of ±25. Moreover, the present forecast says that, cycle 24 will be having double peak with a strong second peak compared to the first peak. This hypothesis is found to be true with the realized data of cycle 24. Further, this techniques have been validated by predicting sunspot cycles 22 and 23. A preliminary level prediction of sunspot cycle 25 also been carried out using the technique presented here. Present study predicts that, cycle 25 also will be a modest cycle like the present cycle 24, and the peak amplitude may vary in a band of 75-95.

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“…Those parameters could be estimated by subjecting to a non-linear least square fit by the Levenberg-Marquardt method (Hiremath, 2006;Press et al, 1992). Frequency and damping rate in Eq.…”

Section: Second-pass Segmentation Algorithm and Model Fittingmentioning

confidence: 99%