A method for the prediction of relative sunspot number for the remainder of a progressing cycle with application to cycle 23

Res. Astron. Astrophys.

Zong

et al. 2009

We find that the solar cycles 9, 11, and 20 are similar to cycle 23 in their respective descending phases. Using this similarity and the observed data of smoothed monthly mean sunspot numbers (SMSNs) available for the descending phase of cycle 23, we make a date calibration for the average time sequence made of the three descending phases of the three cycles, and predict the start of March or April 2008 for cycle 24. For the three cycles, we also find a linear correlation of the length of the descending phase of a cycle with the difference between the maximum epoch of this cycle and that of its next cycle. Using this relationship along with the known relationship between the rise-time and the maximum amplitude of a slowly rising solar cycle, we predict the maximum SMSN of cycle 24 of 100.2±7.5 to appear during the period from May to October 2012.

Section: Discussionmentioning

confidence: 99%

Predicting the start and maximum amplitude of solar cycle 24 using similar phases and a cycle grouping

Res. Astron. Astrophys.

Zong

et al. 2009

“…3 For longterm ongoing cycle predictions, people usually use the curvefitting function (Waldmeier 1935;Hathaway et al 1994;Li 1999;Du 2011;Li et al 2017) or use the similarity of solar cycles, e.g., Li et al (2002).…”

Section: Introductionmentioning

confidence: 99%

Predictability of the Solar Cycle Over One Cycle

Jiang

Jiao

et al. 2018

ApJ

The prediction of the strength of future solar cycles is of interest because of its practical significance for space weather and as a test of our theoretical understanding of the solar cycle. The Babcock-Leighton mechanism allows predictions by assimilating the observed magnetic field on the surface. Since the emergence of sunspot groups has random properties, making it impossible to accurately predict the solar cycle and strongly limiting the scope of cycle predictions, we develop a scheme to investigate the predictability of the solar cycle over one cycle. When a cycle has been ongoing for more than three years, the sunspot group emergence can be predicted along with its uncertainty during the rest time of the cycle. The method for this prediction is to start by generating a set of random realizations that obey the statistical relations of the sunspot emergence. We then use a surface flux transport model to calculate the possible axial dipole moment evolutions. The correlation between the axial dipole moment at cycle minimum and the subsequent cycle strength and other empirical properties of solar cycles are used to predict the possible profiles of the subsequent cycle. We apply this scheme to predict the large-scale field evolution from 2018 to the end of cycle 25, whose maximum strength is expected to lie in the range from 93 to 155 with a probability of 95%.

“…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. They found that cycle 2 is the similar cycle of cycle 23.…”

mentioning

confidence: 99%

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

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

Miao

Liu

et al. 2008

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