MAG4 versus alternative techniques for forecasting active region flare productivity

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

doi:10.1002/2013sw001024

Cited by 52 publications

(49 citation statements)

References 35 publications

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“…However, since the underlying mechanisms leading to the generation of solar eruptions have not yet been indisputably determined, no sufficient conditions of solar eruptivity have yet been established. Solar flare predictions are thus still strongly driven by empirical methods (e.g., Yu et al 2010;Falconer et al 2011Falconer et al , 2014Barnes et al 2016). Nowadays most predictions rely on the determination and characterization of solar active regions through the use of several parameters and the statistical comparison with the historical eruptivity of past active regions presenting similar values of these parameters.…”

Section: Introductionmentioning

confidence: 99%

Relative magnetic helicity as a diagnostic of solar eruptivity

Pariat

Leake

Valori³

et al. 2017

A&A

101

153

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Context. The discovery of clear criteria that can deterministically describe the eruptive state of a solar active region would lead to major improvements on space weather predictions. Aims. Using series of numerical simulations of the emergence of a magnetic flux rope in a magnetized coronal, leading either to eruptions or to stable configurations, we test several global scalar quantities for the ability to discriminate between the eruptive and the non-eruptive simulations. Methods. From the magnetic field generated by the three-dimensional magnetohydrodynamical simulations, we compute and analyze the evolution of the magnetic flux, of the magnetic energy and its decomposition into potential and free energies, and of the relative magnetic helicity and its decomposition. Results. Unlike the magnetic flux and magnetic energies, magnetic helicities are able to markedly distinguish the eruptive from the non-eruptive simulations. We find that the ratio of the magnetic helicity of the current-carrying magnetic field to the total relative helicity presents the highest values for the eruptive simulations, in the pre-eruptive phase only. We observe that the eruptive simulations do not possess the highest value of total magnetic helicity. Conclusions. In the framework of our numerical study, the magnetic energies and the total relative helicity do not correspond to good eruptivity proxies. Our study highlights that the ratio of magnetic helicities diagnoses very clearly the eruptive potential of our parametric simulations. Our study shows that magnetic-helicity-based quantities may be very efficient for the prediction of solar eruptions.

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Section: Introductionmentioning

confidence: 99%

Relative magnetic helicity as a diagnostic of solar eruptivity

Pariat

Leake

Valori³

et al. 2017

A&A

101

153

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“…A complex photosphere causes a complex corona, a complex corona stores energy, and this stored energy is released as solar flares ). There have been many searches for a connection between coronal activity (flares) and the photospheric magnetic field (Ahmed et al 2013;Falconer et al 2011;Mason & Hoeksema 2010;Yuan et al 2010;Huang et al 2010;Jing et al 2010Jing et al , 2006Yu et al 2010a,b;Welsch et al 2009;Ireland et al 2008;Conlon et al 2008;Georgoulis & Rust 2007;Leka & Barnes 2007;Schrijver 2007;Wang 2006;Barnes & Leka 2006;Guo et al 2006;McAteer et al 2005a;Abramenko 2005;Meunier 2004;Abramenko et al 2003;Leka & Barnes 2003b;Hagyard et al 1999;Zhang et al 1994). Despite such research, a fully consistent and causal relationship has not yet been found (e.g., see conclusions in Mason & Hoeksema 2010;Leka & Barnes 2007;and Hagyard et al 1999).…”

Section: Introductionmentioning

confidence: 99%

“…Despite such research, a fully consistent and causal relationship has not yet been found (e.g., see conclusions in Mason & Hoeksema 2010;Leka & Barnes 2007;and Hagyard et al 1999). Nevertheless, there is optimism that photospheric measures may yield some insight into imminent eruptive behavior (e.g., Falconer et al 2011;Yu et al 2010a;Schrijver 2007;Guo et al 2006;Jing et al 2006;Abramenko 2005;Leka & Barnes 2003b).…”

Section: Introductionmentioning

confidence: 99%

“…Many of the studies of the photospheric magnetic field extract a single parameter, or a few (≤7) parameters, and look for a relation to solar flare activity (Falconer et al 2011;Mason & Hoeksema 2010;Yuan et al 2010;Huang et al 2010;Jing et al 2010;Yu et al 2010a;Ireland et al 2008;Conlon et al 2008;Georgoulis & Rust 2007;Schrijver 2007;Wang 2006;Guo et al 2006;Jing et al 2006;McAteer et al 2005a;Abramenko 2005;Meunier 2004;Abramenko et al 2003;Hagyard et al 1999;Zhang et al 1994). By using full vector magnetograms Leka & Barnes 2007, 2003b; Barnes & Leka 2006;Hagyard et al 1999;Zhang et al 1994) to probe the transverse field it is possible to extract a larger number of parameters of magnetic complexity, albeit at the consequence of smaller datasets.…”

Section: Introductionmentioning

confidence: 99%

“…By using full vector magnetograms Leka & Barnes 2007, 2003b; Barnes & Leka 2006;Hagyard et al 1999;Zhang et al 1994) to probe the transverse field it is possible to extract a larger number of parameters of magnetic complexity, albeit at the consequence of smaller datasets. Many of these studies focus on a predictive time window of 24 h (Ahmed et al 2013;Falconer et al 2011;Yuan et al 2010;Jing et al 2010;Leka & Barnes 2007;Schrijver 2007;McAteer et al 2005a) although it is not clear that any of the extracted parameters are optimum for a 24-h predictive A&A 579, A64 (2015) time window. To the best of our knowledge, there are no studies that combine a large dataset with pattern recognition and classification techniques to study the time windows of predictions for each parameter.…”

Section: Introductionmentioning

confidence: 99%

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An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Al-Ghraibah

Boucheron

McAteer

2015

A&A

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Aims. We study the photospheric magnetic field of ∼2000 active regions over solar cycle 23 to search for parameters that may be indicative of energy build-up and its subsequent release as a solar flare in the corona. Methods. We extract three sets of parameters: (1) snapshots in space and time: total flux, magnetic gradients, and neutral lines; (2) evolution in time: flux evolution; and (3) structures at multiple size scales: wavelet analysis. This work combines standard pattern recognition and classification techniques via a relevance vector machine to determine (i.e., classify) whether a region is expected to flare (≥C1.0 according to GOES). We consider classification performance using all 38 extracted features and several feature subsets. Classification performance is quantified using both the true positive rate (the proportion of flares correctly predicted) and the true negative rate (the proportion of non-flares correctly classified). Additionally, we compute the true skill score which provides an equal weighting to true positive rate and true negative rate and the Heidke skill score to allow comparison to other flare forecasting work. Results. We obtain a true skill score of ∼0.5 for any predictive time window in the range 2 to 24 h, with a true positive rate of ∼0.8 and a true negative rate of ∼0.7. These values do not appear to depend on the predictive time window, although the Heidke skill score (<0.5) does. Features relating to snapshots of the distribution of magnetic gradients show the best predictive ability over all predictive time windows. Other gradient-related features and the instantaneous power at various wavelet scales also feature in the top five (of 38) ranked features in predictive power. It has always been clear that while the photospheric magnetic field governs the coronal non-potentiality (and hence likelihood of producing a solar flare), photospheric magnetic field information alone is not sufficient to determine this in a unique manner. Furthermore we are only measuring proxies of the magnetic energy build up. We are still lacking observational details on why energy is released at any particular point in time. We may have discovered the natural limit of the accuracy of flare predictions from these large scale studies.

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Forecasting SEP events with same active region prior flares

Kahler¹,

Ling

White³

2015

Space Weather

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Forecasting large solar energetic (E > 10 MeV) particle (SEP) events is currently based on observed solar X‐ray flare peak fluxes or fluences. Recent work has indicated that the probability of a solar eruptive event in an active region (AR) is enhanced when a large flare has occurred in that AR during the previous day. In addition, peak intensities Sp of SEP events associated with fast coronal mass ejections (CMEs) are larger for CMEs with prior CMEs from the same associated ARs in the previous day. This suggests that the associated SEP event probability and/or Sp may be higher for a given solar X‐ray flare with a recent prior major flare in the same AR. We use data sets of NOAA flares and SEP events from solar cycles 22–24 to test this idea statistically for periods of prior flares ranging from 12 to 48 h. The occurrence probabilities and Sp of large SEP events for flares with prior same AR major (≥ M2) flares are not significantly higher than for flares without the prior flares; hence, prior flare occurrence is not a useful SEP event forecasting tool. The flare‐based occurrence probabilities are higher for cycle 24 than for cycles 22 and 23, but the dependence of Sp on X‐ray fluence appears unchanged. We show an example of a recent flare‐prolific AR for which the SEP‐associated flares are spatially distinct from the numerous non‐SEP associated flares, indicating how prior AR flares may be unrelated to SEP‐associated flares.

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MAG4 versus alternative techniques for forecasting active region flare productivity

Cited by 52 publications

References 35 publications

Relative magnetic helicity as a diagnostic of solar eruptivity

Relative magnetic helicity as a diagnostic of solar eruptivity

An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Forecasting SEP events with same active region prior flares

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