Distribution of the Daily Sunspot Number Variation for the Last 14 Solar Cycles

Pop, Mihail-Ioan

doi:10.1007/s11207-011-9871-x

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…New analysis methods (e.g., Pop, 2012 ; Arlt et al , 2013 ; Leussu et al , 2013 ) have also been developed and these continue to reveal errors and inconsistencies in the various sunspot number records. As this is being written, there is a significant effort within the solar physics community to reconcile the differences in the sunspot numbers and to provide a more reliable sunspot record (with error estimates) from 1610 to the present.…”

Section: Solar Activity Datamentioning

confidence: 99%

The Solar Cycle

Hathaway

2015

Living Rev. Sol. Phys.

825

505

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The solar cycle is reviewed. The 11-year cycle of solar activity is characterized by the rise and fall in the numbers and surface area of sunspots. A number of other solar activity indicators also vary in association with the sunspots including; the 10.7 cm radio flux, the total solar irradiance, the magnetic field, flares and coronal mass ejections, geomagnetic activity, galactic cosmic ray fluxes, and radioisotopes in tree rings and ice cores. Individual solar cycles are characterized by their maxima and minima, cycle periods and amplitudes, cycle shape, the equatorward drift of the active latitudes, hemispheric asymmetries, and active longitudes. Cycle-to-cycle variability includes the Maunder Minimum, the Gleissberg Cycle, and the Gnevyshev-Ohl (even-odd) Rule. Short-term variability includes the 154-day periodicity, quasi-biennial variations, and double-peaked maxima. We conclude with an examination of prediction techniques for the solar cycle and a closer look at cycles 23 and 24.Electronic Supplementary MaterialSupplementary material is available for this article at 10.1007/lrsp-2015-4.

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Section: Solar Activity Datamentioning

confidence: 99%

The Solar Cycle

Hathaway

2015

Living Rev. Sol. Phys.

825

505

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“…New analysis methods (e.g. Pop, 2012;Arlt et al, 2013;Leussu et al, 2013) have also been developed and these continue to reveal errors and inconsistencies in the various sunspot number Hoyt and Schatten (1998) (green), and by SILSO in 2013 (black). These sunspot numbers have disagreements as late as 1900.…”

Section: Revised Sunspot Numbersmentioning

confidence: 99%

“…New analysis methods (e.g. Pop, 2012;Arlt et al, 2013;Leussu et al, 2013) have also been developed and these continue to reveal errors and inconsistencies in the various sunspot number records. As this is being written, there is a significant effort within the solar physics community to reconcile the differences in the sunspot numbers and to provide a more reliable sunspot record (with error estimates) from 1610 to the present.…”

Section: Revised Sunspot Numbersmentioning

confidence: 99%

The Solar Cycle

Hathaway

2010

Living Rev. Sol. Phys.

472

345

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The Solar Cycle is reviewed. The 11-year cycle of solar activity is characterized by the rise and fall in the numbers and surface area of sunspots. We examine a number of other solar activity indicators including the 10.7 cm radio flux, the total solar irradiance, the magnetic field, flares and coronal mass ejections, geomagnetic activity, galactic cosmic ray fluxes, and radioisotopes in tree rings and ice cores that vary in association with the sunspots. We examine the characteristics of individual solar cycles including their maxima and minima, cycle periods and amplitudes, cycle shape, and the nature of active latitudes, hemispheres, and longitudes. We examine long-term variability including the Maunder Minimum, the Gleissberg Cycle, and the Gnevyshev-Ohl Rule. Short-term variability includes the 154-day periodicity, quasi-biennial variations, and double peaked maxima. We conclude with an examination of prediction techniques for the solar cycle.

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“…Compared to the classical Maximum Entropy Method using power law conditions, the presented method has some advantages: (i) local conditions allow improving the estimation locally and also measuring its quality with ε (c k ); (ii) the discretised pdf is well suited for a heuristic optimisation approach such as the SA, even for high N p , because there are no intrinsic parameters to be determined (the Lagrange multipliers of the classical approach), which the SA has difficulty finding; (iii) the pdf estimate lies between a uniform pdf, obtained for k H → ∞ and the experimental values pdf, obtained for k H = 0 and σ → 0; a bad pdf estimate obtained for too small k H or σ can be improved by smoothing; (iv) for large N the method works well for σ far from optimal. The method was applied to the study of some asteroid parameters [5] and solar cycles [6].…”

Section: Experimental Verifications and Conclusionmentioning

confidence: 99%

Maximum entropy estimation of probability distributions with Gaussian conditions

Pop

2012

Preprint

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We describe a method to computationally estimate the probability density function of a univariate random variable by applying the maximum entropy principle with some local conditions given by Gaussian functions. The estimation errors and optimal values of parameters are determined. Experimental results are presented. The method estimates the distribution well if a large enough selection is used, typically at least 1 000 values. Compared to the classical approach of entropy maximisation, local conditions allow improving estimation locally. The method is well suited for a heuristic optimisation approach.

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Distribution of the Daily Sunspot Number Variation for the Last 14 Solar Cycles

Cited by 8 publications

References 22 publications

The Solar Cycle

The Solar Cycle

The Solar Cycle

Maximum entropy estimation of probability distributions with Gaussian conditions

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