Space weather events may cause damage to several fields, including aviation, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are one of the most significant events, and refer to sudden radiation releases that can affect the Earth's atmosphere within a few hours or minutes. Therefore, it is worth designing high-performance systems for forecasting such events. Although in the literature there are many approaches for flare forecasting, there is still a lack of consensus concerning the techniques used for designing these systems. Seeking to establish some standardization while designing flare predictors, in this study we propose a novel methodology for designing such predictors, further validated with extreme gradient boosting tree classifiers and time series. This methodology relies on the following well-defined machine learning based pipeline: (i) univariate feature selection; (ii) randomized hyper-parameter optimization; (iii) imbalanced data treatment; (iv) adjustment of cut-off point of classifiers; and (v) evaluation under operational settings. To verify our methodology effectiveness, we designed and evaluated three proof-of-concept models for forecasting $\geq C$ class flares up to 72 hours ahead. Compared to baseline models, those models were able to significantly increase their scores of true skill statistics (TSS) under operational forecasting scenarios by 0.37 (predicting flares in the next 24 hours), 0.13 (predicting flares within 24-48 hours), and 0.36 (predicting flares within 48-72 hours). Besides increasing TSS, the methodology also led to significant increases in the area under the ROC curve, corroborating that we improved the positive and negative recalls of classifiers while decreasing the number of false alarms.
The EU funded the FLARECAST project, that ran from Jan 2015 until Feb 2018. FLARECAST had a R2O focus, and introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different ML techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort ori…
We describe here the application of a machine learning method for flare forecasting using vectors of properties extracted from images provided by the Helioseismic and Magnetic Imager in the Solar Dynamics Observatory (SDO/HMI). We also discuss how the method can be used to quantitatively assess the impact of such properties on the prediction process.
A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we describe methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multi-day periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods, and conversely, of correctly predicting declining flare activity…
Solar flares create adverse space weather impacting space and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single physical pathway. Studies indicate that multiple physical properties contribute to active region flare potential, compounding the challenge. Recent developments in machine learning (ML) have enabled analysis of higher-dimensional data leading to increasingly better flare forecasting techniques. However, consensus on high-performing flare predictors remains …
We describe a new tool developed for solar flare forecasting on the base of some sunspot group properties. Assuming that the flare frequency follows the Poisson statistics, this tool uses a database containing the morphological characteristics of the suspot groups daily observed by the Equatorial Spar of INAF $-$ Catania Astrophysical Observatory since January 2002 up today. By means of a linear combination of the flare rates computed on the base of some properties of the sunspot groups, like area, number of pores and sunspots, Zurich class, relative importance between leading spot and density…
Most solar flares originate in sunspot groups, where magnetic field changes lead to energy build-up and release. However, few flare-forecasting methods use information of sunspot-group evolution, instead focusing on static point-in-time observations. Here, a new forecast method is presented based upon the 24-hr evolution in McIntosh classification of sunspot groups. Evolution-dependent $\geqslant$C1.0 and $\geqslant$M1.0 flaring rates are found from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle 22; SC22) before converting to probabilities assuming Poisson statistics…
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 …
We study the predictive capabilities of magnetic feature properties (MF) generated by Solar Monitor Active Region Tracker (SMART) for solar flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 that has been previously studied by Ahmed et al. (2011) and a subset of that dataset which only includes detections that are NOAA active regions (ARs). Main contributions: we use marginal relevance as a filter feature selection method to identify most useful SMART MF properties for separating flaring from non-flaring detections and logistic regression to derive clas…
FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories（中国科学院国家天文台光学天文重点实验室）；Zidong Taichu (Beijing) Technology Co., Ltd.（智控（北京）科技有限公司）
Operational Solar Flare Forecasting System Using an Explainable Large Language Model
State Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences（中国科学院国家空间科学中心太阳活动与空间天气全国重点实验室）；Jiangsu University of Science and Technology（江苏科技大学）；University of Malaya（马来亚大学）
Improving the performance of solar flare forecasting is a hot topic in solar physics research field. Deep learning has been considered a promising approach to perform solar flare forecasting in recent years. We first used the Generative Adversarial Networks (GAN) technique augmenting sample data to balance samples with different flare classes. We then proposed a hybrid convolutional neural network (CNN) model M for forecasting flare eruption in a solar cycle. Based on this model, we further investigated the effects of the rising and declining phases for flare forecasting. Two CNN models, i.e.,…
Solar flares produce radiation which can have an almost immediate effect on the near-Earth environment, making it crucial to forecast flares in order to mitigate their negative effects. The number of published approaches to flare forecasting using photospheric magnetic field observations has proliferated, with varying claims about how well each works. Because of the different analysis techniques and data sets used, it is essentially impossible to compare the results from the literature. This problem is exacerbated by the low event rates of large solar flares. The challenges of forecasting rare…
Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region observables, most commonly line-of-sight or vector magnetograms of the active-region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the…
We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-real-time (NRT), taken over a five-year interval (2012 - 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit…
Solar energetic particle (SEP) events are known to occur following solar flares and coronal mass ejections (CMEs). However some high-energy solar events do not result in SEPs being detected at Earth, and it is these types of event which may be termed "false alarms". We define two simple SEP forecasting algorithms based upon the occurrence of a magnetically well-connected CME with a speed in excess of 1500 km/s ("a fast CME") or a well-connected X-class flare and analyse them with respect to historical data sets. We compare the parameters of those solar events which produced an enhancement of >…
The solar X-ray irradiance is significantly heightened during the course of a solar flare, which can cause radio blackouts due to ionization of the atoms in the ionosphere. As the duration of a solar flare is not related to the size of that flare, it is not directly clear how long those blackouts can persist. Using a random forest regression model trained on data taken from X-ray light curves, we have developed a direct forecasting method that predicts how long the event will remain above background levels. We test this on a large collection of flares observed with GOES-15, and show that it ge…
Indications are presented for a significant connection between the relative motion of the planets and the appearance of energetic solar flares. Based on the records of the last four decades, the analysis highlights remarkable features and a lack of randomness in the data. The indications are supported further by the predictive power of a preliminary application to forecasting with machine learning methods.
University of Campinas (UNICAMP)（坎皮纳斯大学）；Federal Institute of Education, Science and Technology of Rio Grande do Sul (IFRS)（南里奥格兰德州联邦教育、科学与技术学院）
Disturbances in space weather can negatively affect several fields, including aviation and aerospace, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are the most significant events that can affect the Earth's atmosphere, thus leading researchers to drive efforts on their forecasting. The related literature is comprehensive and holds several systems proposed for flare forecasting. However, most techniques are tailor-made and designed for specific purposes, not allowing researchers to customize them in case of changes in data i…
INAF - Osservatorio Astrofisico di Torino（意大利国家天体物理研究所-都灵天体物理天文台）
Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence. This study shows that video-based deep learning can be used for operational purposes when the training and validation sets used for the network optimization are generated while accounting for the periodicity of the solar cycle. Specifically, the paper describes an algorithm that can be applied to build up sets of active regions that are balanced according to the flare class rates associated to a specific cycle phase. Th…
A classification infrastructure built upon Discriminant Analysis has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional Discriminant Analysis, an extension through Bayes' theorem to probabil…
Solar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology (Barnes et al, 2016, Paper…
One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate…
The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms; forecasts for each active region on the solar disk over the next 24 hours, and full-disk forecasts for the next four days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verific…
The Space-Weather ANalytics for Solar Flares (SWAN-SF) is a multivariate time series benchmark dataset recently created to serve the heliophysics community as a testbed for solar flare forecasting models. SWAN-SF contains 54 unique features, with 24 quantitative features computed from the photospheric magnetic field maps of active regions, describing their precedent flare activity. In this study, for the first time, we systematically attacked the problem of quantifying the relevance of these features to the ambitious task of flare forecasting. We implemented an end-to-end pipeline for preproce…
Solar flare forecasting can be realized by means of the analysis of magnetic data through artificial intelligence techniques. The aim is to predict whether a magnetic active region (AR) will originate solar flares above a certain class within a certain amount of time. A crucial issue is concerned with the way the adopted machine learning method is implemented, since forecasting results strongly depend on the criterion with which training, validation, and test sets are populated. In this paper we propose a general paradigm to generate these sets in such a way that they are independent from each…
A workshop was recently held at Nagoya University (31 October - 02 November 2017), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today's operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019) we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behav…
Solar magnetic activity produces extreme solar flares and coronal mass ejections, which pose grave threats to electronic infrastructure and can significantly disrupt economic activity. It is therefore important to appreciate the triggers of explosive solar activity and develop reliable space-weather forecasting. Photospheric vector-magnetic-field data capture sunspot magnetic-field complexity and can therefore improve the quality of space-weather prediction. However, state-of-the-art vector-field observations are consistently only available from Solar Dynamics Observatory/Helioseismic and Magn…
The prediction of solar flares is still a significant challenge in space weather research, with no techniques currently capable of producing reliable forecasts performing significantly above climatology. In this paper, we present a flare forecasting technique using data assimilation coupled with computationally inexpensive cellular automata called sandpile models. Our data assimilation algorithm uses the simulated annealing method to find an optimal initial condition that reproduces well an energy-release time series. We present and empirically analyze the predictive capabilities of three sand…
Advances and Challenges in Solar Flare Prediction: A Review
State Key Laboratory of Solar Activity and Space Weather, NAOC（国家天文台太阳活动与空间天气国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；College of Physics and Optoelectronics, Taiyuan University of Technology（太原理工大学物理与光电工程学院）
PRESOL: a web-based computational setting for feature-based flare forecasting
MIDA, Dipartimento di Matematica, Università di Genova（MIDA，数学系，热那亚大学）；Institute for Data Science, University of Applied Sciences and Arts Northwestern Switzerland（数据科学研究所，西北瑞士应用科学与艺术大学）；HoB srl（HoB公司）；Istituto Nazionale di Astrofisica, Osservatorio Astrofisico di Torino（国家天文物理研究所，都灵天文物理观测站）
Instituto De Astrofisica E Ciências Do Espaço, University of Coimbra（科英布拉大学天体物理与空间科学研究所）；University of Rome La Sapienza（罗马第一大学（萨皮恩扎大学））；University of Rome Tor Vergata（罗马第二大学（托尔韦尔加塔大学））；MIDA（MIDA机构）
We present the results from the first ensemble prediction model for major solar flares (M and X classes). The primary aim of this investigation is to explore the construction of an ensemble for an initial prototyping of this new concept. Using the probabilistic forecasts from three models hosted at the Community Coordinated Modeling Center (NASA-GSFC) and the NOAA forecasts, we developed an ensemble forecast by linearly combining the flaring probabilities from all four methods. Performance-based combination weights were calculated using a Monte-Carlo-type algorithm that applies a decision thre…
'Flare Likelihood and Region Eruption Forecasting (FLARECAST)' is a Horizon 2020 project, which realized a technological platform for machine learning algorithms, with the objective of providing the space weather community with a prediction service for solar flares. This paper describes the FLARECAST service and shows how the methods implemented in the platform allow both flare prediction and a quantitative assessment of how the information contained in the space data utilized in the analysis may impact the forecasting process.
We analyze the relationship between the flare X-ray peak flux, and characteristics of the Polarity Inversion Line (PIL) and Active Regions (AR), derived from line-of-sight (LOS) magnetograms. The PIL detection algorithm based on a magnetogram segmentation procedure is applied for each AR with 1 hour cadence. The PIL and AR characteristics are associated with the AR flare history and divided into flaring and non-flaring cases. Effectiveness of the derived characteristics for flare forecasting is determined by the number of non-flaring cases separated from flaring cases by a certain threshold, a…
Georgia State University（佐治亚州立大学）；Academy of Athens（雅典学院）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem. Taking full advantage of pre-flare time series in solar active regions is made possible via the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark dataset; a partitioned collection of multivariate time series of active region properties comprising 4075 regions and spanning over 9 years of the Solar Dynamics Observatory (SDO) period of operations. We showcase the general concept of temporal coherence triggered by th…
Solar flares are explosions in the solar atmosphere that release intense bursts of short-wavelength radiation and are capable of producing severe space-weather consequences. Flares release free energy built up in coronal fields, which are rooted in active regions (ARs) on the photosphere, via magnetic reconnection. The exact processes that lead to reconnection are not fully known and therefore reliable forecasting of flares is challenging. Recently, photospheric magnetic-field data has been extensively analysed using machine learning (ML) and these studies suggest that flare-forecasting accura…
The Geostationary Orbital Environmental Satellites (GOES) Soft X-ray (SXR) sensors have provided data relating to, inter alia, the time, intensity and duration of solar flares since the 1970s. The GOES SXR Flare List has become the standard reference catalogue for solar flares and is widely used in solar physics research and space weather. We report here that in the current version of the list there are significant differences between the mean duration of flares which occurred before May 1997 and the mean duration of flares thereafter. Our analysis shows that the reported flare timings for the…
In this study we determine scaling relationships of observed solar flares that can be used to predict upper limits of the GOES-class magnitude of solar flares. The flare prediction scheme is based on the scaling of the slowly-varying potential energy $E_p(t)$, which is extrapolated in time over an interval of $Δt \le$ 24 hrs. The observed scaling of the dissipated energy $E_{diss}$ scales with the potential field energy as $E_{diss} \propto E_p^{1.32}$. In addition, the observed scaling relationship of the flare volume, $V \propto E_{diss}^{1.17}$, the multi-thermal energy, $E_{th} \propto V^{…
Computer Science Georgia State University Atlanta, USA（计算机科学 乔治亚州立大学 奥斯汀, 美国）；Computer Science Texas Christian University Texas, USA（计算机科学 德克萨斯基督教大学 德克萨斯, 美国）
