Prediction of Solar Eruptions Using Filament Metadata

Aggarwal, Ashna; Schanche, N.; Reeves, Katharine K.; Kempton, Dustin J.; Angryk, Rafal A.

doi:10.3847/1538-4365/aab77f

Cited by 23 publications

(10 citation statements)

References 43 publications

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“…Wang et al, 2003;Veronig et al, 2006b;Vršnak et al, 2006b;Miklenic et al, 2007;Pötzi et al, 2015;Thalmann et al, 2015;Joshi et al, 2017;Temmer et al, 2017;Hinterreiter et al, 2018;Tschernitz et al, 2018), filament eruptions and dynamics (e.g. Maričić et al, 2004;Möstl et al, 2009;Zuccarello et al, 2012;Su et al, 2012;Filippov, 2013;Yardley et al, 2016;Aggarwal et al, 2018), Moreton waves (e.g. Pohjolainen et al, 2001;Warmuth et al, 2001;Vršnak et al, 2006a;Veronig et al, 2006a), as well as for long-term evolution of the distributions of prominences and polar crown filaments (e.g.…”

Section: Introductionmentioning

confidence: 99%

Kanzelhöhe Observatory: Instruments, Data Processing and Data Products

et al. 2021

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Kanzelhöhe Observatory for Solar and Environmental Research (KSO) of the University of Graz (Austria) is in continuous operation since its foundation in 1943. Since the beginning, its main task was the regular observation of the Sun in full disc. In this long time span covering almost seven solar cycles, a substantial amount of data was collected, which is made available online. In this article we describe the separate processing steps from data acquisition to high level products for different observing wavelengths. First of all we present in detail the quality classification, which is important for further processing of the raw images. We show how we construct centre-to-limb variation (CLV) profiles and how we remove large scale intensity variations produced by the telescope optics in order to get images with uniform intensity and contrast. Another important point is an overview of the different data products from raw images to high contrast images with heliographic grids overlaid. As the data products are accessible via different sources, we also present how to get information about the availability and how to obtain these data. Finally, in an appendix, we describe in detail the information in the FITS headers, the file naming and the data hierarchy.

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Section: Introductionmentioning

confidence: 99%

Kanzelhöhe Observatory: Instruments, Data Processing and Data Products

et al. 2021

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“…Yu et al (2009) and Yuan et al (2010) used line-of-sight magnetogram data obtained from Michelson Doppler Imager (MDI) for flare prediction. Aggarwal et al (2018) used filament metadata for the prediction of eruptive events. However, most of the studies used vector magnetogram data obtained from the Helioseismic Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) for the purpose of flare prediction.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing between flaring and nonflaring active regions

Hazra

Sardar

Chowdhury

2020

A&A

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Context. Large-scale solar eruptions significantly affect space weather and damage space-based human infrastructures. It is necessary to predict large-scale solar eruptions; it will enable us to protect the vulnerable infrastructures of our modern society. Aims. We investigate the difference between flaring and nonflaring active regions. We also investigate whether it is possible to forecast a solar flare. Methods. We used photospheric vector magnetogram data from the Solar Dynamic Observatory’s Helioseismic Magnetic Imager to study the time evolution of photospheric magnetic parameters on the solar surface. We built a database of flaring and nonflaring active regions observed on the solar surface from 2010 to 2017. We trained a machine-learning algorithm with the time evolution of these active region parameters. Finally, we estimated the performance obtained from the machine-learning algorithm. Results. The strength of some magnetic parameters such as the total unsigned magnetic flux, the total unsigned magnetic helicity, the total unsigned vertical current, and the total photospheric magnetic energy density in flaring active regions are much higher than those of the non-flaring regions. These magnetic parameters in a flaring active region evolve fast and are complex. We are able to obtain a good forecasting capability with a relatively high value of true skill statistic. We also find that time evolution of the total unsigned magnetic helicity and the total unsigned magnetic flux provides a very high ability of distinguishing flaring and nonflaring active regions. Conclusions. We can distinguish a flaring active region from a nonflaring region with good accuracy. We confirm that there is no single common parameter that can distinguish all flaring active regions from the nonflaring regions. However, the time evolution of the top two magnetic parameters, the total unsigned magnetic flux and the total unsigned magnetic helicity, have a very high distinguishing capability.

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“…McCauley et al (2015) studied the kinematics of more than one hundred limb prominence eruptions and found that the decay index at the onset of the fast-rise is in the range 0.7-2 with a mean value of n c  1.1. The average decay index for eruptive filaments in 2012 --2013 in the study of Aggarwal et al (2018) has fairly broad distributions in the range 0 --2, with a mean value of 0.84. Sarkar et al (2019) found that before the eruption on 2010 June 13 the top of a polar crown prominence reached the height with the decay index value of 1.2, while the decay-index value at the corresponding cavity centroid height reached the critical limit of 1.4 for the onset of torus instability.…”

Section: Introductionmentioning

confidence: 89%

Critical decay index for eruptions of ‘short’ filaments

Filippov

2021

Monthly Notices of the Royal Astronomical Society

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Model of a partial current-carrying torus loop anchored to the photosphere is analysed. Conditions of the catastrophic loss of equilibrium are considered and corresponding value of the critical decay index of external magnetic field is found. Taking into account line-tying conditions leads to non-monotonous dependence of the critical decay index on the height of the apex and length of the flux rope (its endpoints separation). For relatively short flux ropes, the critical decay index is significantly lower than unity, which is in contrast to widespread models with the typical critical decay index above unity. The steep decrease of the critical index with height at low heights is due to the sharp increase of the curvature of the flux-rope axis that transforms from a nearly straight line to a crescent.

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Prediction of Solar Eruptions Using Filament Metadata

Cited by 23 publications

References 43 publications

Kanzelhöhe Observatory: Instruments, Data Processing and Data Products

Kanzelhöhe Observatory: Instruments, Data Processing and Data Products

Distinguishing between flaring and nonflaring active regions

Critical decay index for eruptions of ‘short’ filaments

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