Verification of a Similar Cycle Prediction for the Ascending and Peak Phases of Solar Cycle 23

Wang, Jia-Long; Gong, Jianhua; Liu, Siqing; Le, G. M.; Han, Yanni; Sun, Jiayi

doi:10.1088/1009-9271/2/5/396

Cited by 14 publications

(10 citation statements)

References 2 publications

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“…3, the small circles represent the forecast results and the thick line gives the observed values. It can be seen that the forecast results agree well with the observed values (Wang et al, 2002). In 1999, Zhang and Wang (1999) predicted the maximum monthly smoothed sunspot number in solar cycle 23 with a linear regressive method.…”

Section: Long-term Predictionsupporting

confidence: 66%

Solar activity prediction studies and services in NAOC

He¹,

Wang²,

Du³

et al. 2008

Advances in Space Research

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Section: Long-term Predictionsupporting

confidence: 66%

Solar activity prediction studies and services in NAOC

He¹,

Wang²,

Du³

et al. 2008

Advances in Space Research

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“…In the same way as discussed above, 7 solar cycles including cycles 8,9,11,17,18,20 and 21 are selected as the similar cycles to solar cycle 23. Thus, the calculated i A and i A Δ can be used as the predictions of i R and the error for cycle 23 [15] , respectively.…”

Section: Similar Cycle Methods and Verification Of The Prediction Of Smentioning

confidence: 99%

“…Wang and Han [15,16] proposed a similar cycle method based on the conception that, generally, we may find not only one but more cycles similar to a certain solar cycle. The application of the method to cycle 23 results in very successful predictions of SMSNs (errors ≤ 30%) for the 96 months from August 1996 to July 2004 [17,18] . Several methods have been used to make this kind of prediction and Li et al [19] proposed a method for the prediction of SMSNs for the remainder of a progressing solar cycle.…”

mentioning

confidence: 99%

Prediction of the smoothed monthly mean sunspot numbers for solar cycle 24

Wang

Miao

Liu

et al. 2008

Sci. China Ser. G-Phys. Mech. Astron.

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The prediction for the smoothed monthly mean sunspot numbers (hereafter SMSNs) of solar cycle 23, which was given with a similar cycle method proposed by us at the beginning time of cycle 23, is analyzed and verified in this paper. Using our predicted maximum SMSN and the ascending length for solar cycle 24, and assuming their relative errors to be respectively 20% and ± 7 months, solar cycles 2, 4, 8, 11, 17, 20 and 23 are selected to be the similar cycles to cycle 24. The selected solar cycles are divided into two groups. The first group consists of all the selected cycles; while the second group consists of only cycles 11, 17, 20 and 23. Two SMSN time profiles then may be obtained, respectively, for the two similar cycle groups. No significant difference is found between the two predicted time profiles. Considering the latest observed sunspot number so far available for cycle 23 and the predictions for the minimum SMSN of cycle 24, a date calibration is done for the obtained time profiles, and thus, SMSNs for 127 months of cycle 24, from October 2007 to April 2018, are predicted. solar cycle, sunspot numbers, prediction of cycle 24A Wolf sunspot number, or the so-called relative sunspot number, is usually used as one of the solar indices describing solar activity, which is made of the number of sunspots and the number of sunspot groups [1] . The number is obviously lack of physical meanings compared, for examples, with the solar flux at 2800 MHz, the total area of sunspots [2] , and the group sunspot number [3] . The relative sunspot number has a historical record longer than 200 years, its time sequence reflects clearly the periodicities of solar activity [4] , and the number has close relations with the productivities of solar activities such as solar flares [5] and coronal mass ejections [6,7] . So, the number is used in the fields of solar physics, geophysics and space weather more frequently than other indices. The Wolf number is often simply called sunspot number. However, in studies and predictions of long-term variations of solar activities, the smoothed monthly mean sunspot number (hereafter

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“…Plethora of literatures is available where the authors have attempted to describe the cycles as a periodic phenomenon (e.g. Denkmayr and Cugnon, 1997;Hanslmeier et al, 1999;Wang et al, 2002;Zhan et al, 2003;Zhou et al, 2002;Xu et al, 2008). Understanding the solar activity cycle remains a key unsolved problem in solar physics (along with, e.g., heating of the solar corona and solar flares).…”

Section: Introductionmentioning

confidence: 99%

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

Chattopadhyay

Chattopadhyay²

2012

Eur. Phys. J. Plus

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This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

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Verification of a Similar Cycle Prediction for the Ascending and Peak Phases of Solar Cycle 23

Cited by 14 publications

References 2 publications

Solar activity prediction studies and services in NAOC

Solar activity prediction studies and services in NAOC

Prediction of the smoothed monthly mean sunspot numbers for solar cycle 24

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

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