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Same as Figure 4, but for the effective separation. Again, note the difference between ARs associated with flares and those that are not. The effective separation remains roughly constant if no flaring occurs, whereas it tends to reach a minimum value near the time of flaring if flaring does occur. A steady decrease prior to flaring is followed by a very gentle recovery period. The size of this response is appreciably larger for X-class flares.
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Flare occurrence is statistically associated with changes in several characteristics of the line-of-sight magnetic field in solar active regions (ARs). We calculated magnetic measures throughout the disk passage of 1075 ARs spanning solar cycle 23 to find a statistical relationship between the solar magnetic field and flares. This expansive study o...
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Context 1
... effective separation ( Figure 5) shows a similar flare dependency to GWILL; however, the general trend is now inverted. The separation definitely decreases prior to flaring and increases very slightly after. ...
Context 2
... GWILL (Figure 4, lower right panel), the standard deviation is relatively large indicating a lack of signal, as expected. However, standard deviation is not as large in effective separation and total flux ( Figures 5 and 6, lower right panel). This difference is likely due to GWILL values that are closer to its fundamental noise level. ...
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... Although a lot of effort has been devoted to flare prediction (Huang, X. et al., 2013;Panos & Kleint, 2020;Georgoulis et al., 2021;Tang et al., 2021), developing accurate, operational near-real-time flare forecasting systems remains a challenge. In the past, researchers designed statistical models for the prediction of flares based on the physical properties of active regions (Gallagher et al., 2002;Leka & Barnes, 2007;Mason & Hoeksema, 2010). With the availability of large amounts of flare-related data (Georgoulis et al., 2021), researchers started using machine learning methods for flare forecasting (Bobra & Couvidat, 2015;Liu et al., 2017;Abduallah et al., 2021a). ...
Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. In this paper, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a gamma-class flare within the next 24 to 72 hours. We consider three gamma classes, namely the >=M5.0 class, the >=M class and the >=C class, and build three transformers separately, each corresponding to a gamma class. Each transformer is used to make predictions of its corresponding gamma-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.
... Table 4. The Heidke Skill Score (HSS) as defined by the Space Weather Prediction Center, also known as HSS 2 , is used by [40]. This measure expresses how much better the forecast is than a random one. ...
Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence Learner (Mr-SEQL), and a Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over nine years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the True Skill Statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MiniRocket, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics.
... Historically, most of the effort in solar flare research and flare forecasting has been focused on analyzing the photospheric magnetic field measurements, considering that full vectorfield component data are only available for the photosphere. Many research workers have studied the correlations between different photospheric magnetic field parameters and flare characteristics, as well as the evolution of those parameters during solar eruptions (e.g., Leka and Barnes, 2003;2007;Mason and Hoeksema, 2010;Wang et al., 2012;Kazachenko et al., 2017;Whitney Aegerter et al., 2020). Strong relationships have been found, for example, between the Geostationary Operational Environmental Satellites (GOES) measured peak X-ray flux and the flare ribbon reconnection flux ; however, the relationships typically involve post-eruption data and therefore are not directly applicable for flare forecasting. ...
... The analysis of a particular case study (Garland et al., 2023) was adjusted and extended to a larger dataset of strong/extreme (M-and X-class) solar flare events. Following the modeling and parameter calculation for each event, a statistical analysis similar to that of Mason and Hoeksema (2010) and Kazachenko et al. (2022) was performed. Specifically, a superposed epoch analysis was applied to the temporal evolution of the magnetic field parameters during a time interval of 1 hour before through 1 hour after the flare occurrence. ...
... Once the parameter data for all 30 flare events had been calculated following the methodology outlined in Section 3, superposed epoch (SPE) plots, similar to that proposed by Mason and Hoeksema (2010), were created to examine any consistent trends within the time series of the different magnetic field parameters. Additionally, correlation coefficients between the parameters and flare properties were calculated using only data prior to the start of the flares in order to find relationships between the state of the 3D solar magnetic field leading up to an eruption and the flare characteristics. ...
Using non-linear force free field (NLFFF) extrapolation, 3D magnetic fields were modeled from the 12-min cadence Solar Dynamics Observatory Helioseismic and Magnetic Imager (HMI) photospheric vector magnetograms, spanning a time period of 1 hour before through 1 hour after the start of 18 X-class and 12 M-class solar flares. Several magnetic field parameters were calculated from the modeled fields directly, as well as from the power spectrum of surface maps generated by summing the fields along the vertical axis, for two different regions: areas with photospheric | B z |≥ 300 G (active region—AR) and areas above the photosphere with the magnitude of the non-potential field ( B NP ) greater than three standard deviations above | B N P | ̄ of the AR field and either the unsigned twist number | T w | ≥ 1 turn or the shear angle Ψ ≥ 80° (non-potential region—NPR). Superposed epoch (SPE) plots of the magnetic field parameters were analyzed to investigate the evolution of the 3D solar field during the solar flare events and discern consistent trends across all solar flare events in the dataset, as well as across subsets of flare events categorized by their magnetic and sunspot classifications. The relationship between different flare properties and the magnetic field parameters was quantitatively described by the Spearman ranking correlation coefficient, r s . The parameters that showed the most consistent and discernable trends among the flare events, particularly for the hour leading up to the eruption, were the total unsigned flux ϕ ), free magnetic energy ( E Free ), total unsigned magnetic twist ( τ Tot ), and total unsigned free magnetic twist ( ρ Tot ). Strong (| r s | ∈ [0.6, 0.8)) to very strong (| r s | ∈ [0.8, 1.0]) correlations were found between the magnetic field parameters and the following flare properties: peak X-ray flux, duration, rise time, decay time, impulsiveness, and integrated flux; the strongest correlation coefficient calculated for each flare property was 0.62, 0.85, 0.73, 0.82, −0.81, and 0.82, respectively.
... Each parameter is either a scalar or a vector characterizing the state of a certain active region at a certain time. There have been hundreds of parameters proposed over the last 20 years (Falconer et al., 2002;Leka and Barnes, 2003a;Abramenko, 2005;McAteer et al., 2005;Georgoulis and Rust, 2007;Conlon et al., 2010;McAteer et al., 2010;Mason and Hoeksema, 2010;Reinard et al., 2010;Bobra et al., 2014;Korsó s et al., 2015;Kontogiannis et al., 2017;Guennou et al., 2017;Murray et al., 2018;Park et al., 293(8),; Guerra et al., 2018;Kontogiannis et al., 2019;Kusano et al., 2020;Georgoulis et al., 2021;Leka et al., 2023, and others). Parameters or properties with a potential predictive capability and their rationale are discussed in Section 2.3. ...
Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big Data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big Data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the plethora of methods is typically far from perfect, with physical reasons to blame, besides practical shortcomings: imperfect data, data gaps and a lack of multiple, and meaningful, vantage points of solar observations. We briefly discuss these issues, too, that shape our desired short- and long-term objectives for an efficient future predictive capability. A central aim of this article is to trigger meaningful, targeted discussions that will compel the community to adopt standards for performance verification and validation, which could be maintained and enriched by institutions such as NASA’s Community Coordinated Modeling Center (CCMC) and the community-driven COSPAR/ISWAT initiative.
... Magnetic field parameters (e.g., helicity, flux, etc.) were developed to find relationships between the photospheric magnetic field states and the following solar activities. Most of the recent prediction models use the near-continuous stream of vector magnetogram data produced by SDO (Mason & Hoeksema 2010). Ahmadzadeh et al. (2021) proposed a machine-learning-based method to train a flare prediction model for predicting solar flares. ...
Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes.
... False Negative True Negative [31] utilize a definition of the Heidke Skill Score (HSS) proposed by the Space Weather Prediction Center, denoted as HSS2, which quantifies the enhancement of the forecast compared to a random forecast. HSS2 is calculated using the following formula: ...
Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and caused by the changes in magnetic field states in solar active regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares ranging from electronic communication disruption to radiation exposure-based health risks to the astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MINIROCKET), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations SEQuence Learner (Mr-SEQL), and Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the on the Space Weather ANalytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over 9 years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the true skill statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MINIROCKET, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics.
... The main PIL (MPIL) separates the major polarity regions of an active region. Falconer et al. (2003) studied the CME predictability of the measured MPIL, while Mason & Hoeksema (2010) found that it could also be used for flare prediction purposes. They suggested that a large solar eruption could be anticipated within a two-day period if the MPIL exceeds the threshold of 62 Mm, where the observed transverse field strength is greater than 150 Gauss. ...
... To analyze the evolution of the 3D magnetic field structure in the LSA of these two active regions, we selected six promising prediction parameters and analyzed the two active regions during the disk passage in the LSA. Five out of the six selected parameters were based on the FLARECAST project proposed by Georgoulis et al. (2021): the MPIL (Falconer et al. 2003;Mason & Hoeksema 2010), the unsigned magnetic flux of the polarity inversion line, R log (Schrijver 2007), the effective connected magnetic field, B eff (Georgoulis & Rust 2007), the gradientweighted length of strong-field PILs, WL SG (Falconer et al. 2012), and the Ising energy, E Ising (Ahmed et al. 2010). Last but not least, another widely used parameter is the magnetic helicity flux Soós et al. 2022;Liu et al. 2023). ...
The three-dimensional (3D) coronal magnetic field has not yet been directly observed. However, for a better understanding and prediction of magnetically driven solar eruptions, 3D models of solar active regions are required. This work aims to provide insight into the significance of different extrapolation models for analyzing the preeruptive conditions of active regions with morphological parameters in 3D. Here, we employed potential field (PF), linear force-free field (LFFF), and nonlinear force-free field (NLFFF) models and a neural network-based method integrating observational data and NLFFF physics (NF2). The 3D coronal magnetic field structure of a “flaring” (AR11166) and “flare-quiet” (AR12645) active region, in terms of their flare productivity, is constructed via the four extrapolation methods. To analyze the evolution of the field, six prediction parameters were employed throughout, from the photosphere up to the base of the lower corona. First, we find that the evolution of the adopted morphological parameters exhibits similarity across the investigated time period when considering the four types of extrapolations. Second, all the parameters exhibited preeruptive conditions not only at the photosphere but also at higher altitudes in the case of active region (AR) 11166, while three out of the six proxies also exhibited preeruptive conditions in the case of AR12645. We conclude that: (i) the combined application of several different precursor parameters is important in the lower solar atmosphere to improve eruption predictions, and (ii) to gain a quick yet reliable insight into the preflare evolution of active regions in 3D, the PF and LFFF are acceptable; however, the NF2 method is likely the more suitable option.
... Finally, we define data from each SC as different training/ testing data sets (discussed in Section 2.5) to use with SVM and XGBoost algorithms. Predictive capabilities are then measured using true skill statistics (TSS), Heidke skill scores (HSS 2 , developed by Mason & Hoeksema 2010), and recall metrics. We also apply k-fold cross validation (CV) to optimize our models with respect to TSS. ...
Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from one and two SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores 2 , even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.
... It is well established from previous research that the magnetic properties of active regions (ARs) such as the magnetic flux, the shear angle, the magnetic free-energy density, the magnetic polarity inversion lines, and so on, are related to solar eruptions (see Lü et al. 1993;Schmieder et al. 1994;Pevtsov et al. 1995;Schölkopf et al. 1998;Titov & Démoulin 1999;Falconer et al. 2002;Moon et al. 2002;Leka & Barnes 2003; Barnes & Leka 2006;Cui et al. 2006;Jing et al. 2006;LaBonte et al. 2007;Leka & Barnes 2007;Schrijver 2007;Falconer et al. 2008;Magara & Tsuneta 2008;Leka et al. 2009;Schrijver 2009;Mason & Hoeksema 2010;Fisher et al. 2012;Moore et al. 2012;Vasantharaju et al. 2018). The Helioseismic and Magnetic Imager (HMI; Scherrer et al. 2012;Schou et al. 2012;Centeno et al. 2014;Hoeksema et al. 2014) vector magnetic field product, the Space Weather HMI Active Region Patches (SHARP; Bobra et al. 2014), consists of the vector magnetic field images and many SHARP parameters, which are typical magnetic properties of ARs calculated from a single patch, and have been widely used to study solar eruptions and develop prediction models. ...
It is well accepted that the physical properties obtained from the solar magnetic field observations of active regions (ARs) are related to solar eruptions. These properties consist of temporal features that might reflect the evolution process of ARs, and spatial features that might reflect the graphic properties of ARs. In this study, we generated video data sets with timescales of 1 day and image data sets of the SHARP radial magnetic field of the ARs from 2010 May to 2020 December. For the ARs that evolved from “quiet” to “active” and erupted the first strong flares in 4 days, we extract and investigate both the temporal and spatial features of ARs from videos, aiming to capture the evolution properties of their magnetic field structures during their transition process from “quiet” (non–strong flaring) to “active” (strong flaring). We then conduct a comparative analysis of the model performance by video input and single-image input, as well as of the effect of the model performance variation with the prediction window up to 3 days. We find that for those ARs that erupted the first strong flares in 4 days, the temporal features that reflect their evolution from “quiet” to “active” before the first strong flares can be recognized and extracted from the video data sets by our network. These features turn out to be important predictors that can effectively improve strong-flare prediction, especially by reducing the false alarms in a nearly 2 day prediction window.
... Although a lot of effort has been devoted to flare prediction 12-15 , developing accurate, operational near-realtime flare forecasting systems remains a challenge. In the past, researchers designed statistical models for the prediction of flares based on the physical properties of active regions [16][17][18] . With the availability of large amounts of flare-related data 14 , researchers started using machine learning methods for flare forecasting 3,19,20 . ...
Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a $$\gamma$$ γ -class flare within the next 24 to 72 h. We consider three $$\gamma$$ γ classes, namely the $$\ge$$ ≥ M5.0 class, the $$\ge$$ ≥ M class and the $$\ge$$ ≥ C class, and build three transformers separately, each corresponding to a $$\gamma$$ γ class. Each transformer is used to make predictions of its corresponding $$\gamma$$ γ -class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.
