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A solar activity precursor technique of spotless event has been currently used to predict the strengths and the times of rise of the 11-year coming cycles. This simple statistical method has been previously applied to predict the maximum amplitudes and the times of rises of cycles 22 and 23. The results obtained are successful for both cycles. A developed version of the suggested method was previously used to make an early forecast of the characteristic parameters of the cycle 24. In this work the preliminarily predicted parameters of the cycle 24 are checked using observed values of the spotless events. In addition, the developed method is also applied to forecast the maximum amplitude and time of rise of the 25th solar cycle. The maximum Wolf number and time of rise of the latter cycle are found to be 118.2 and 4.0 years respectively.

The good quality of the observing sequence of about 60 photographs of the whitelight corona taken during the total solar eclipse observations on 29 March 2006, in Al Sallum, Egypt, enable us to use a new method of image processing for enhancement of the fine structure of coronal phenomena. We present selected magnetic-field lines derived for different parameters of the extrapolation model. The coincidence of the observed coronal white-light fine structures and the computed field-line positions provides a 3D causal relationship between coronal structures and the coronal magnetic field.

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A suggested method is proposed to forecast the general features of the 11-year solar activity principle cycle. It is based upon the count of lengths and durations of spotless events, prevailing in the preceding minimum of the coming new cycle. The method has been successfully applied to predict the strengths and time of rises for the 22 nd and 23 rd 11-year cycles. The proposed precursor technique is further developed to make preliminary prediction of the maximum relative sunspot number and the time of rise of the 24 th 11-year cycle. The predicted values of these parameters are found to be 90.7 ± 9.2 and 4.6 ± 1.2 year respectively. In addition, neural, Fuzzy neural and genetic algorithms have been also applied for the confirmation of the predicted results. A comparison with the early predictions used by other methods is given.