During the total solar eclipse of July 22, 2009, we carried out a white-light observation in Anji, Zhejiang, China. The aim wasto observe the polar plumes (PPs) with high spatial and temporal resolutions in white-light. With the observational data, weinvestigate the properties and evolution of the PPs and compare them with those of the low-latitude plumes (LPs). We find thatboth the PPs and the LPs have comparable lengths and widths, and the mean length and width are 300 Mm and 16 Mm, re-spectively. The average inclination angle (13 degree) of the PPs is smaller than that (32 degree) of the LPs. Generally, theplumes which are closer to the coronal hole center are more vertical. We trace the PPs and the LPs in the sequence of imagesand find that none of them disappears and no new one is created. Additionally, neither plasma outflow nor transverse oscilla-tion is observed. These imply that the evolution process of plumes is much longer than the timescale of eclipse.
Three new longitudinal magnetic field parameters are extracted from SOHO/MDI magnetograms to characterize properties of the stressed magnetic field in active regions, and their flare productivities are calculated for 1055 active regions. We find that the proposed parameters can be used to distinguish flaring samples from non-flaring samples. Using the long-term accumulated MDI data, we build the solar flare prediction model by using a data mining method. Furthermore, the decision boundary, which is used to divide flaring from non-flaring samples, is determined by the decision tree algorithm. Finally, the performance of the prediction model is evaluated by 10-fold cross validation technology. We conclude that an efficient solar flare prediction model can be built by the proposed longitudinal magnetic field parameters with the data mining method.
We analyzed the correlation of the solar magnetograms and Dopplergrams from SOHO/MDI and SDO/HMI respectively. It is found that the full disk correlation coefficient of Dopplergrams is more than 0.80 between SOHO/MDI and SDO/HMI. The full disk correlation coefficient of magnetograms is about 0.73 and is more than 0.95 for active regions only. We also analyzed the distribution of the cross helicity （velocity-magnetic-field correlation） on the solar surface. It is found that the latitude distributions of the cross helicity based on SOHO/MDI data and SDO/HMI data have similar tendencies, and in the analysis of solar active regions the amplitude of the horizontal component of the mean cross helicity is about two times the line-of-sight one.
With the aim of studying the relationship between the relative motions of the loop-top （LT） source and footpoints （FPs） during the rising phase of solar flares, we give a detailed analysis of the X7.1 class flare that occurred on 2005 January 20. The flare was clearly observed by RHESSI, showing a distinct X-ray flaring loop with a bright LT source and two well-defined hard X-ray （HXR） FPs. In particular, we correct the projection effect for the positions of the FPs and magnetic polarity inversion line. We find that： （1） The LT source showed an obvious U-shaped trajectory. The source of the higher energy LT shows a faster downward/upward speed. （2） The evolution of FPs was temporally correlated with that of the LT source. The converging/separating motion of FPs corresponds to the downward/upward motion of the LT source. （3） The initial flare shear of this event is found to be nearly 50 degrees, and it has a fluctuating decrease throughout the contraction phase as well as the expansion phase. （4） Four peaks of the time profile of the unshearing rate are found to be temporally correlated with peaks in the HXR emission flux. This flare supports the overall contraction pic- ture of flares： a descending motion of the LT source, in addition to converging and unshearing motion of FPs. All results indicate that the magnetic field was very highly sheared before the onset of the flare.
Tuan-Hui ZhouJun-Feng WangDong LiQi-Wu SongVictor MelnikovHai-Sheng Ji
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.
An ensemble prediction model of solar proton events （SPEs）, combining the information of solar flares and coronal mass ejections （CMEs）, is built. In this model, solar flares are parameterized by the peak flux, the duration and the longitude. In addition, CMEs are parameterized by the width, the speed and the measurement position angle. The importance of each parameter for the occurrence of SPEs is estimated by the information gain ratio. We find that the CME width and speed are more informative than the flare’s peak flux and duration. As the physical mechanism of SPEs is not very clear, a hidden naive Bayes approach, which is a probability-based calculation method from the field of machine learning, is used to build the prediction model from the observational data. As is known, SPEs originate from solar flares and/or shock waves associated with CMEs. Hence, we first build two base prediction models using the properties of solar flares and CMEs, respectively. Then the outputs of these models are combined to generate the ensemble prediction model of SPEs. The ensemble prediction model incorporating the complementary information of solar flares and CMEs achieves better performance than each base prediction model taken separately.
Using the Hilbert-Huang Transform method, we investigate the periodic- ity in the monthly occurrence numbers and monthly mean energy of coronal mass ejections （CMEs） observed by the Large Angle and Spectrometric Coronagraph Experiment on board the Solar and Heliographic Observatory from 1999 March to 2009 December. We also investigate the periodicity in the monthly occurrence numbers of Hα flares and monthly mean flare indices from 1996 January to 2008 December. The results show the following. （1） The period of 5.66 yr is found to be statistically significant in the monthly occurrence numbers of CMEs; the period of 10.5 yr is found to be statistically significant in the monthly mean energy of CMEs. （2） The periods of 3.05 and 8.70 yr are found to be statistically significant in the monthly occurrence numbers of Hα flares; the period of 9.14 yr is found to be statistically significant in the monthly mean flare indices.
A method of calculating the induced electric field is presented. The induced electric field in the solar atmosphere is derived by the time variation of the magnetic field when the accumulation of charged particles is neglected. In order to derive the spatial distribution of the magnetic field, several extrapolation methods are introduced. With observational data from the Helioseismic and Magnetic Imager aboard NASA＇s Solar Dynamics Observatory taken on 2010 May 20, we extrapolate the magnetic field from the photosphere to the upper atmosphere. By calculating the time variation of the magnetic field, we can get the induced electric field. The derived induced electric field can reach a value of 102 V cm-1 and the average electric field has a maximum point at the layer 360 km above the photosphere. The Monte Carlo method is used to compute the triple integration of the induced electric field.
