A Stochastic Prediction Model for the Sunspot Cycles

Sabarinath, A.; Anilkumar, A. K.

doi:10.1007/s11207-011-9861-z

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, we considered these cycles for modeling the double peak phenomena of cycle 24. Table 5 gives the model parameters (location, scale and area parameters) as given in Sabarinath and Anilkumar (2011) of all the slow rising cycles. Further, the shape model for predicting cycle 24 is taken as the average by averaging of model parameters for the cycles 1, 5, 6, 7, 14, 16, and 17.…”

Section: Prediction Improvementmentioning

confidence: 99%

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

Sabarinath

Anilkumar

2018

J Earth Syst Sci

View full text Add to dashboard Cite

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.

show abstract

Section: Prediction Improvementmentioning

confidence: 99%

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

Sabarinath

Anilkumar

2018

J Earth Syst Sci

View full text Add to dashboard Cite

show abstract

“…In prediction process, averaged models are used as an initial estimate of the future cycle. An averaged sunspot number cycle shape is used explicitly in the methods based on the work of McNish and Lincoln, while implicit average cycle shape models are used in neural network techniques [9,10]. All these shape models are developed by estimating accurately the model parameters after the completion of a cycle.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sunspot Cycle 24 Based on Geometric Indices of Sunspot Cycles 17 to 23

Sabarinath,

Anilkumar

2023

joast

Self Cite

View full text Add to dashboard Cite

Prediction of the peak amplitude, duration of the rise time, and the length of Sunspot Cycle 24 are made through a statistical analysis using some of the derived parameters called geometric indices of Sunspot Cycles 17 to 23. The geometric parameters considered are: The rate of the ascent phase, rate of the descent phase, maximum amplitude, rise time, fall time, and length. These parameters are computed from cycles 17 to 23. Then, the pair wise correlation between these six parameters computed. A strong correlation is found between the rate of ascent phase and amplitude, rate of the ascent phase and rise time, and the rise time and length. The linear regression models derived from these correlations are directly used to predict the peak amplitude, duration of the rise time, and the length of sunspot cycle 24.The rate of rise computed from the initial 30 months of data is enough to predict the peak amplitude, epoch of the occurrence of peak amplitude and total length of the cycle. The predicted features of smoothed sunspot cycle 24 are: Maximum amplitude of cycle is 73.52 ± 10.6 numbers, length of cycle is 148 ± 7.3 months, i.e., cycle 24 ends between December 2020 and April 2022, and the occurrence of maximum amplitude is 52 ± 3.1 months, i.e, cycle 24 reach to peak amplitude between January 2013 and July 2013.

show abstract

Predictions of Solar Cycle 24: How are we doing?

Pesnell

2016

Space Weather

View full text Add to dashboard Cite

Predictions of solar activity are an essential part of our Space Weather forecast capability.Users are requiring usable predictions of an upcoming solar cycle to be delivered several years before solar minimum. A set of predictions of the amplitude of Solar Cycle 24 accumulated in 2008 ranged from zero to unprecedented levels of solar activity. The predictions formed an almost normal distribution, centered on the average amplitude of all preceding solar cycles. The average of the current compilation of 105 predictions of the annual-average sunspot number is 106 ± 31, slightly lower than earlier compilations but still with a wide distribution. Solar Cycle 24 is on track to have a below-average amplitude, peaking at an annual sunspot number of about 80. Our need for solar activity predictions and our desire for those predictions to be made ever earlier in the preceding solar cycle will be discussed. Solar Cycle 24 has been a below-average sunspot cycle. There were peaks in the daily and monthly averaged sunspot number in the Northern Hemisphere in 2011 and in the Southern Hemisphere in 2014. With the rapid increase in solar data and capability of numerical models of the solar convection zone we are developing the ability to forecast the level of the next sunspot cycle. But predictions based only on the statistics of the sunspot number are not adequate for predicting the next solar maximum. I will describe how we did in predicting the amplitude of Solar Cycle 24 and describe how solar polar field predictions could be made more accurate in the future.

show abstract

A Stochastic Prediction Model for the Sunspot Cycles

Cited by 4 publications

References 10 publications

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

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

Prediction of Sunspot Cycle 24 Based on Geometric Indices of Sunspot Cycles 17 to 23

Predictions of Solar Cycle 24: How are we doing?

Contact Info

Product

Resources

About