A tool for empirical forecasting of major flares, coronal mass ejections, and solar particle events from a proxy of active‐region free magnetic energy

Falconer, D. A.; Barghouty, A. F.; Khazanov, Igor; Moore, R. L.

doi:10.1029/2009sw000537

Cited by 94 publications

(71 citation statements)

References 28 publications

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“…Using the ground-based data from the University of Hawai'i Vector Magnetograph (Mickey et al 1996), Leka & Barnes (2003a) used only pixels above 3r detections. Falconer et al (2008) imposed the B z component to be greater than 150 G, while Falconer et al (2011) used either a threshold Figure 6 displays the same parameter evolutions as Figure 5, but excluding pixels for which the total magnetic field B is lower than 100 G. The seven simulations exhibit the same evolution over time, reaching similar values close to the eruption starting time. In comparison with the previous results using a lower mask threshold, the rise observed for the six MPIL-related parameters of the eruptive simulations is lost, and the characteristic eruptivity signature is no longer discernible.…”

Section: Influence Of Data Maskingmentioning

confidence: 49%

“…Many studies investigated the link between changes in some photospheric parameters and the coronal eruptive and flaring activity, by investigating prior temporal changes, performing superposed epoch analysis, or forecasting the likelihood of the flaring events (see e.g., Bao et al 1999;Leka & Barnes, 2003a;Schrijver, 2007;Jing et al 2010;Mason & Hoeksema, 2010;Falconer et al 2011;Ahmed et al 2013;Bobra & Ilonidis, 2016, and references therein), with moderate results. More recently, Al-Ghraibah et al (2015) studied the magnetic parameters of about 2000 ARs to search for the best eruptive predictor.…”

Section: Introductionmentioning

confidence: 99%

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Testing predictors of eruptivity using parametric flux emergence simulations

Guennou

Pariat

Leake

et al. 2017

J. Space Weather Space Clim.

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Solar flares and coronal mass ejections (CMEs) are among the most energetic events in the solar system, impacting the near-Earth environment. Flare productivity is empirically known to be correlated with the size and complexity of active regions. Several indicators, based on magnetic field data from active regions, have been tested for flare forecasting in recent years. None of these indicators, or combinations thereof, have yet demonstrated an unambiguous eruption or flare criterion. Furthermore, numerical simulations have been only barely used to test the predictability of these parameters. In this context, we used the 3D parametric magnetohydrodynamic (MHD) numerical simulations of the self-consistent formation of the flux emergence of a twisted flux tube, inducing the formation of stable and unstable magnetic flux ropes of Leake et al. (2013Leake et al. ( , 2014. We use these numerical simulations to investigate the eruptive signatures observable in various magnetic scalar parameters and provide highlights on data analysis processing. Time series of 2D photospheric-like magnetograms are used from parametric simulations of stable and unstable flux emergence, to compute a list of about 100 different indicators. This list includes parameters previously used for operational forecasting, physical parameters used for the first time, as well as new quantities specifically developed for this purpose. Our results indicate that only parameters measuring the total non-potentiality of active regions associated with magnetic inversion line properties, such as the Falconer parameters L ss , WL ss , L sg , and WL sg , as well as the new current integral WL sc and length L sc parameters, present a significant ability to distinguish the eruptive cases of the model from the non-eruptive cases, possibly indicating that they are promising flare and eruption predictors. A preliminary study about the effect of noise on the detection of the eruptive signatures is also proposed.

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Section: Influence Of Data Maskingmentioning

confidence: 49%

Section: Introductionmentioning

confidence: 99%

Testing predictors of eruptivity using parametric flux emergence simulations

Guennou

Pariat

Leake

et al. 2017

J. Space Weather Space Clim.

View full text Add to dashboard Cite

show abstract

“…The ionosphere is similarly disturbed by any M or X flare that happens anywhere on the disk, regardless of whether the eruption also produces a CME (e.g., Suess & Tsurutani 1998). Most M or X flares are produced together with a CME, and about half of these CMEs are fast CMEs (Falconer et al 2011). The closer the source active region is to the disk center the more nearly Earth-centered the swath of the CME is likely to be, and the greater the CME's impact on the magnetosphere is likely to be (e.g., Moore et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…free-energy proxy measured at the start of the day. This correlation for each of these two kinds of events forecasts from an active region's measured free-energy proxy the chance that the active region will produce an event of that kind in the coming day or so (Falconer et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Prior Flaring as a Complement to Free Magnetic Energy for Forecasting Solar Eruptions

Falconer

Moore

Barghouty

et al. 2012

ApJ

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From a large database of (1) 40,000 SOHO/MDI line-of-sight magnetograms covering the passage of 1300 sunspot active regions across the 30• radius central disk of the Sun, (2) a proxy of each active region's free magnetic energy measured from each of the active region's central-disk-passage magnetograms, and (3) each active region's fulldisk-passage history of production of major flares and fast coronal mass ejections (CMEs), we find new statistical evidence that (1) there are aspects of an active region's magnetic field other than the free energy that are strong determinants of the active region's productivity of major flares and fast CMEs in the coming few days; (2) an active region's recent productivity of major flares, in addition to reflecting the amount of free energy in the active region, also reflects these other determinants of coming productivity of major eruptions; and (3) consequently, the knowledge of whether an active region has recently had a major flare, used in combination with the active region's free-energy proxy measured from a magnetogram, can greatly alter the forecast chance that the active region will have a major eruption in the next few days after the time of the magnetogram. The active-region magnetic conditions that, in addition to the free energy, are reflected by recent major flaring are presumably the complexity and evolution of the field.

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“…Features derived from the line-of-sight magnetogram are useful indicators for future flare prediction, such as the magnetic flux, the gradient of the magnetic field (Yu et al 2009;Steward et al 2011), the length of magnetic neutral lines (Steward et al 2011), the effective magnetic field (Georgoulis & Rust 2007;Papaioannou et al 2015), the unsigned magnetic flux near the magnetic neutral lines (Rvalue: Schrijver 2007;Falconer et al 2011), the total magnetic energy dissipation (Song et al 2009), the weighted magnetic neutral line length and the distance between NS polarity sunspot centers (Mason & Hoeksema 2010), the nonpotentiality (e.g., Falconer et al 2014), and the wavelet spectra (Yu et al 2010;Al-Ghraibah et al 2015;Boucheron et al 2015;Muranushi et al 2015). These features are related to the dynamics of flux emergence and are strongly correlated with the energy storage and the triggering mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

Nishizuka¹,

Sugiura²,

Kubo³

et al. 2017

ApJ

146

135

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We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.

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A tool for empirical forecasting of major flares, coronal mass ejections, and solar particle events from a proxy of active‐region free magnetic energy

Cited by 94 publications

References 28 publications

Testing predictors of eruptivity using parametric flux emergence simulations

Testing predictors of eruptivity using parametric flux emergence simulations

Prior Flaring as a Complement to Free Magnetic Energy for Forecasting Solar Eruptions

Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

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