The maximum amplitude （Rm） of a solar cycle, in the term of mean sunspot numbers, is well-known to be positively correlated with the preceding minimum （Rmin）. So far as the long term trend is concerned, a low level of Rmin tends to be followed by a weak Rm, and vice versa. We found that the evidence is insufficient to infer a very weak Cycle 24 from the very low Rmin in the preceding cycle. This is concluded by analyzing the correlation in the temporal variations of parameters for two successive cycles.
The growth rate of solar activity in the early phase of a solar cycle has been known to be well correlated with the subsequent amplitude （solar maximum）. It provides very useful information for a new solar cycle as its variation reflects the temporal evolution of the dynamic process of solar magnetic activities from the initial phase to the peak phase of the cycle. The correlation coefficient between the solar maximum （Rmax） and the rising rate （βa） at Am months after the solar minimum （Rmin） is studied and shown to increase as the cycle progresses with an inflection point （r = 0.83） at about Am = 20 months. The prediction error of Rmax based on βa is found within estimation at the 90% level of confidence and the relative prediction error will be less than 20% when Am ≥ 20. From the above relationship, the current cycle （24） is preliminarily predicted to peak around October, 2013 with a size of Rmax = 84 ＋ 33 at the 90% level of confidence.
A higher correlation tends to yield a more accurate prediction,so that a correlation as high as possible has been searched for and employed in the prediction of solar activity.Instead of using geomagnetic activity during the descending phase of the solar cycle,the minimum annual aa index (aa min) is used as an indicator for the ensuing maximum amplitude (R m) of the sunspot cycle.A four-cycle periodicity is roughly shown in the correlation between R m and aa min.The widely accepted Ohl's precursor prediction method often fails due to the prediction error relative to its estimated uncertainty.An accurate prediction depends on the positive variation of the correlation rather than a higher correlation.Previous experiences by using this method indicate that a prediction for the next cycle,R m (24)=80 ± 17,is likely to fail,implying that the sunspot maximum of Cycle 24 may be either smaller than 63 or greater than 97.
It is widely believed that the evolution of solar active regions leads to solar flares. However, information about the evolution of solar active regions is not employed in most existing solar flare forecasting models. In the current work, a short- term solar flare forecasting model is proposed, in which sequential sunspot data, in- cluding three days of information about evolution from active regions, are taken as one of the basic predictors. The sunspot area, the Mclntosh classification, the mag- netic classification and the radio flux are extracted and converted to a numerical for- mat that is suitable for the current forecasting model. Based on these parameters, the sliding-window method is used to form the sequential data by adding three days of information about evolution. Then, multi-layer perceptron and learning vector quanti- zation are employed to predict the flare level within 48 h. Experimental results indicate that the performance of the proposed flare forecasting model works better than previ- ous models.
The concept of degree of similarity （η）, is proposed to quantitatively describe the similarity of a parameter （e.g. the maximum amplitude Rmax） of a solar cycle relative to a referenced one, and the prediction method of similar cycles is further developed. For two parameters, the solar minimum （Rmin） and rising rate （βa）, which can be directly measured a few months after the minimum, a synthesis degree of similarity （ηs） is defined as the weighted-average of the η values around Rmin and βa, with the weights given by the coefficients of determination of Rmax with Rmin and βa, respectively. The monthly values of the whole referenced cycle can be predicted by averaging the corresponding values in the most similar cycles with the weights given by the ηs values. As an application, Cycle 24 is predicted to peak around January 2013 i8 （month） with a size of about Rmax = 84 ± 17 and to end around September 2019.
