Solar flare forecasting using morphological properties of sunspot groups

Falco, M.; Costa, P.; Romano, P.

doi:10.1051/swsc/2019019

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…Kumar 2010;Crown 2012;Vadakke Veettil et al 2019;Rodriguez et al 2019;Plainaki et al 2020) or research groups have worked on targeted flare or multi-flare forecasting tools (e.g. Barnes et al 2007;Schrijver 2007;Georgoulis & Rust 2007;Falconer et al 2011;Korsós et al 2015;Romano et al 2015;Florios et al 2018;McCloskey et al 2018;Lim et al 2019;Berrilli et al 2019;Falco et al 2019;Giovannelli et al 2019;Yi et al 2020;Nishizuka et al 2020;Korsós et al 2020;Cinto et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Flare Forecasting Algorithms Based on High-Gradient Polarity Inversion Lines in Active Regions

Cicogna,

Berrilli,

Calchetti

et al. 2021

Preprint

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Solar flares emanate from solar active regions hosting complex and strong bipolar magnetic fluxes. Estimating the probability of an active region to flare and defining reliable precursors of intense flares is an extremely challenging task in the space weather field. In this work, we focus on two metrics as flare precursors, the unsigned flux R, tested on MDI/SOHO data and one of the most used parameters for flare forecasting applications, and a novel topological parameter D representing the complexity of a solar active region. More in detail, we propose an algorithm for the computation of the R value which exploits the higher spatial resolution of HMI maps. This algorithm leads to a differently computed R value, whose functionality is tested on a set of cycle 24th solar flares. Furthermore, we introduce a topological parameter based on the automatic recognition of magnetic polarity-inversion lines in identified active regions, and able to evaluate its magnetic topological complexity. We use both a heuristic approach and a supervised machine learning method to validate the effectiveness of these two descriptors to predict the occurrence of X-or M-class flares in a given solar active region during the following 24 hours period. Our feature ranking analysis shows that both parameters play a significant role in prediction performances. Moreover, the analysis demonstrates that the new topological parameter D is the only one, among 173 overall predictors, which is always present for all test subsets and is systematically ranked within the top-ten positions in all tests concerning the computation of the weighs with which each predictor impacts the flare forecasting.

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Section: Introductionmentioning

confidence: 99%

Flare Forecasting Algorithms Based on High-Gradient Polarity Inversion Lines in Active Regions

Cicogna,

Berrilli,

Calchetti

et al. 2021

Preprint

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“…In the following year, they proposed a statistical method for predicting soft X-ray flare outbursts using the HMM, which also achieved higher accuracy in flare forecasting. Falco et al (2019) described a flare forecasting tool based on sunspot group characteristics, assuming that the frequency of flare eruptions follows Poisson statistics. The tool uses observational instruments to collect the morphological attributes of sunspot groups and then calculates the probability of flare outbursts in active regions.…”

Section: Design Methods Of Forecasting Modelmentioning

confidence: 99%

Research Progress on Solar Flare Forecast Methods Based on Data-driven Models

Han

et al. 2023

Res. Astron. Astrophys.

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Eruption of solar flares is a complex nonlinear process, and the rays and high energy particles generated by such eruption are detrimental to the reliability of space based or ground based systems. So far, there are not reliable physical models to accurately account for the flare outburst mechanism, but a lot of data driven models have been built to study the solar flare and forecast it. In the paper, the status of solar flare forecast is reviewed, with emphasis on the machine learning methods and data processing techniques used in the models. At first, the essential forecast factors strongly relevant to solar flare outbursts, such as classification information of the sunspots and evolution pattern of the magnetic field, are reviewed and analyzed. Subsequently, methods of resampling for data preprocessing are introduced to solve the problems of class imbalance in the solar flare samples. Afterwards, typical model structures adopted for flare forecasting are reviewed from the aspects of the single and fusion models, and the forecast performances of the different models are analyzed. Finally, we herein summarize the current research on solar flare forecasting and outline its development trends.

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“…The occurrence of SFs, eruptive vs. confined, is a subject to ongoing research [34][35][36], including novel approaches using deep/machine learning and neural network methods. A representative description, however, goes beyond the scope of this study.…”

Section: Introductionmentioning

confidence: 99%

M-Class Solar Flares in Solar Cycles 23 and 24: Properties and Space Weather Relevance

Miteva

Samwel

2022

Universe

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A comprehensive statistical analysis on the properties and accompanied phenomena of all M-class solar flares (as measured in soft X-rays) in the last two solar cycles (1996–2019) is presented here with a focus on their space weather potential. The information about the parent active region and the underlying sunspot (Hale) type is collected for each case, where possible, in order to identify photospheric precondition as precursors for the solar flare eruption or confinement. Associations with coronal mass ejections, solar energetic particles, and interplanetary radio emissions are also evaluated and discussed as possible proxies for flare eruption and subsequent space weather relevance. The results show that the majority (∼80%) of the analyzed M-class flares are of β, β-γ, and β-γ-δ magnetic field configuration. The M-class population of flares is accompanied by CMEs in 41% of the cases and about half of the flare sample has been associated with radio emission from electron beams. A much lower association (≲10%) is obtained with shock wave radio signatures and energetic particles. Furthermore, a parametric scheme is proposed in terms of occurrence rates between M-class flares and a variety of accompanied solar phenomena as a function of flare sub-classes or magnetic type. This study confirms the well-known reduced but inevitable space weather importance of M-class flares.

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Solar flare forecasting using morphological properties of sunspot groups

Cited by 13 publications

References 27 publications

Flare Forecasting Algorithms Based on High-Gradient Polarity Inversion Lines in Active Regions

Flare Forecasting Algorithms Based on High-Gradient Polarity Inversion Lines in Active Regions

Research Progress on Solar Flare Forecast Methods Based on Data-driven Models

M-Class Solar Flares in Solar Cycles 23 and 24: Properties and Space Weather Relevance

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