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

Abstract

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

Solar cycle distribution of great geomagnetic storms

“…The amplitudes of solar cycle 24 predicted by researcher are quite dispersed (Obridko and Shelting 2008;Pesnell 2008;Bhatt et al 2009;Ajabshirizadeh 2011;Chumak and Matveychuk 2010;Dabas and Sharma 2010;Du and Wang 2010;Kane 2010;Wang et al, 2002Wang et al, , 2008Wang et al, , 2009). However, a dynamo model tends to predict that the amplitude of solar cycle 24 is about 90 or even at less than 90 (Jiang et al 2007).…”

Predicting the solar maximum with the rising rate

“…A high correlation can be used to estimate the strength of a new solar cycle [9][10][11][12][13][14][15][16][17]. For example, Ohl's geomagnetic precursor method [18] succeeded in predicting R max in cycles 20-22 [19][20][21][22] due to the high correlation coefficients (> 0.8) between R max and geomagnetic-based parameters.…”

Operational Space Weather Services in National Space Science Center of Chinese Academy of Sciences

“…One approach is to develop our own model from the ground up inside NSSC, and the other is to work cooperatively with other institutes or universities. Among the 20 operational models identified as of high priorities, currently, 10 models have been completed and applied to operational services, including solar cycle F 10.7 and sunspot predictions by similar cycle method (see Wang et al [] and Miao et al [] for the prediction of solar cycle 24), 27 day F 10.7 and A p predictions by linear autoregressive method, K p prediction by neural network, and D s t [ Wang et al , ], A E [ Luo et al , ], magnetopause [ Lin et al , ], relativistic electrons at the GEO orbit, and China total electron content (TEC) map predictions by empirical statistical modeling. Six models are still in the development stage, which are for predictions of coronal mass ejection propagation [ Shen et al , ], coronal hole high‐speed streams [ Luo et al , ], plasma sheet [ Luo et al , ], regional TEC assimilation [ Aa et al , ], regional ionospheric index, and 400 km thermosphere mass density models [ Lei et al , ].…”