Comparative case study of two methods to assess the eruptive potential of selected active regions

Solar eruptive events,like flares and coronal mass ejections,are characterized by the rapid release of energy that can give rise to emission of radiation across the entire electromagnetic spectrum and to an abrupt significant increase in the kinetic energy of particles.These energetic phenomena can have important effects on the space weather conditions and therefore it is necessary to understand their origin,in particular,what is the eruptive potential of an active region(AR).In these case studies,we compare two distinct methods that were used in previous works to investigate the variations of some characteristic physical parameters during the pre-flare states of flaring ARs.These methods consider:i)the magnetic flux evolution and magnetic helicity accumulation,and ii)the fractal and multi-fractal properties of flux concentrations in ARs.Our comparative analysis is based on time series of photospheric data obtained by the Solar Dynamics Observatory between March 2011 and June 2013.We selected two distinct samples of ARs:one is distinguished by the occurrence of more energetic M-and X-class flare events,that may have a rapid effect on not just the near-Earth space,but also on the terrestrial environment;the second is characterized by no-flares or having just a few C-and B-class flares.We found that the two tested methods complement each other in their ability to assess the eruptive potentials of ARs and could be employed to identify ARs prone to flaring activity.Based on the presented case study,we suggest that using a combination of different methods may aid to identify more reliably the eruptive potentials of ARs and help to better understand the pre-flare states.

Short-term solar eruptive activity prediction models based on machine learning approaches:A review

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our technosphere.The short-term solar eruptive activity prediction is an active field of research in the space weather prediction.Numerical,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive activities.With the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant progress.In this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.