Ever since the magnetohydrodynamic(MHD)method for extrapolation of the solar coronal magnetic field was first developed to study the dynamic evolution of twisted magnetic flux tubes,it has proven to be efficient in the reconstruction of the solar coronal magnetic field.A recent example is the so-called data-driven simulation method(DDSM),which has been demonstrated to be valid by an application to model analytic solutions such as a force-free equilibrium given by Low and Lou.We use DDSM for the observed magnetograms to reconstruct the magnetic field above an active region.To avoid an unnecessary sensitivity to boundary conditions,we use a classical total variation diminishing Lax-Friedrichs formulation to iteratively compute the full MHD equations.In order to incorporate a magnetogram consistently and stably,the bottom boundary conditions are derived from the characteristic method.In our simulation,we change the tangential fields continually from an initial potential field to the vector magnetogram.In the relaxation,the initial potential field is changed to a nonlinear magnetic field until the MHD equilibrium state is reached.Such a stable equilibrium is expected to be able to represent the solar atmosphere at a specified time. By inputting the magnetograms before and after the X3.4 flare that occurred on 2006 December 13,we find a topological change after comparing the magnetic field before and after the flare.Some discussions are given regarding the change of magnetic configuration and current distribution.Furthermore,we compare the reconstructed field line configuration with the coronal loop observations by XRT onboard Hinode.The comparison shows a relatively good correlation.
In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are extracted from SOHO/MDI longitudinal magnetograms as measures. Based on these pa- rameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Con- sidering the imbalanced problem in dataset, the K-means clustering, as an unsupervised clustering algorithm, is used to convert imbalanced data to balanced ones. Finally, the learning vector quantity is employed to predict the flares level within 48 hours. Experimental results indicate that the performance of the proposed flare forecasting model with sequential data is improved.
The relationships between solar flare parameters (total importance, time duration, flare index, and flux) and sunspot activity (R z ) as well as those between geomagnetic activity (aa index) and the flare parameters can be well described by an integral response model with the response time scales of about 8 and 13 months, respectively. Compared with linear relationships, the correlation coefficients of the flare parameters with R z , of aa with the flare parameters, and of aa with R z based on this model have increased about 6%, 17%, and 47% on average, respectively. The time delays between the flare parameters with respect to R z , aa to the flare parameters, and aa to R z at their peaks in a solar cycle can be predicted in part by this model (82%, 47%, and 78%, respectively). These results may be further improved when using a cosine filter with a wider window. It implies that solar flares are related to the accumulation of solar magnetic energy in the past through a time decay factor. The above results may help us to understand the mechanism of solar flares and to improve the prediction of the solar flares.
ZEUS is a magnetohydrodynamics simulation code widely used in astrophysical research.However,it was recently found that the code may produce artificial shocks in the rarefaction region in some numerical tests since it is not upwinded in fast and slow waves.We propose a method of magnetosonic characteristics to evolve compressional waves.The tests indicate that this method cures the "rarefaction shocks" problem to a large extent and it also greatly reduces some post shock oscillations.
ZHU XiaoShuaiWANG HuaNingFAN YuLiangDU ZhanLeHE Han
