ON THE STATE OF A SOLAR ACTIVE REGION BEFORE FLARES AND CMEs

Abstract

Several attempts have been made to find reliable diagnostic tools to determine the state prior to flares and related coronal mass ejections (CMEs) in solar active regions (ARs). Characterization of the level of mixed states is carried out using the Debrecen sunspot Data for 116 flaring ARs. Conditional flare probabilities (CFPs) are calculated for different flaring classes. The association with slow/fast CMEs is examined. Two precursor parameters are introduced: (i) the sum of the (daily averaged) horizontal magnetic gradient G S (G DS ) and (ii) the separation parameter -S l f . We found that if  there is larger than 60% CFP that C-class flare(s) may have the strongest intensity within 48 hr. Next, from analyzing G S for determining CFP we found: if   G 5.5 log S ( ) 6.5, then it is very likely that C-class flare(s) may be the most intense; if   G 6.5 log 7.5 S ( ) then there is ∼45% CFP that M-class could have the highest intensity; finally, if  G 7.5 log S ( ) then there is at least 70% chance that the strongest energy release will be X-class in the next 48 hr. ARs are unlikely to produce X-class flare(s) if  -S 13 l f and log(G S )  5.5. Finally, in terms of providing an estimate of an associated slow/fast CME, we found that, if  -S log l f ( ) 0.4 or  G log DS ( ) 6.5, there is no accompanying fast CME in the following 24 hr.

Testing and Improving a Set of Morphological Predictors of Flaring Activity

“…The sum of the horizontal magnetic field gradient (Korsós and Erdélyi, 2016), G S , is given by the formula:…”

Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning

“…The S l−f and G S can be determined from the moment of first available observation of sunspot groups, because the applied umbrae data are suitably corrected for geometrical foreshortening in the SOHO/MDI-Debrecen Data (SDD 3, ) and the SDO/HMI-Debrecen Data (HMIDD 4 ) catalogues (Baranyi et al, 2016). Furthermore, these two morphological parameters were shown to be potential indicators for upcoming flares on a smaller number of typical test cases (Korsós and Erdélyi, 2016). The test cases included 116 ARs, which were selected from SDD.…”

“…Furthermore, Kontogiannis et al (2018) investigated and tested some of those parameters, which were identified as efficient flare predictors. These parameters include, e.g.,, a quantity denoted as B eff that measures the coronal magnetic connectivity between the opposite magnetic field elements (Georgoulis and Rust, 2007), Ising energy E Ising of a distribution of interacting magnetic elements (Ahmed et al, 2010), the sum of the horizontal magnetic field gradient G S (Korsós and Erdélyi, 2016), and the total unsigned nonneutralized currents, I NN,tot (Kontogiannis et al, 2017).…”

“…Section 4 shows the results of the analysis focusing on two morphological parameters in particular, while our conclusions are in Section 5. Korsós and Erdélyi (2016) introduced and tested, as a trial, an advantageous scheme that may be used as new prediction indicators besides the Zürich, McIntosh and Mount Wilson classification systems. This scheme includes two morphological parameters, namely:…”

Predicting the Loci of Solar Eruptions

“…The existing flare and CME forecasting tools are usually based on the local dynamics of active regions, such as the magnetic topology (e.g., Korsós et al 2014, Korsós & Erdélyi 2016. However, certain global phenomena are also able to provide opportunities for predicting the locus of solar eruptive events, such as the longitudinal inhomogeneous properties of active regions (e.g., Bumba et al 1965;Bogart 1982;Berdyugina et al 2006;Balthasar 2007;Zhang et al 2011).…”