A Comparison of Flare Forecasting Methods, I: Results from the "All-Clear" Workshop
G. Barnes, K. D. Leka, C. J. Schrijver, T. Colak, R. Qahwaji, O. W. Ashamari, Y. Yuan, J. Zhang, R. T. J. McAteer, D. S. Bloomfield, P. A. Higgins, P. T. Gallag… 展开作者
G. Barnes, K. D. Leka, C. J. Schrijver, T. Colak, R. Qahwaji, O. W. Ashamari, Y. Yuan, J. Zhang, R. T. J. McAteer, D. S. Bloomfield, P. A. Higgins, P. T. Gallagher, D. A. Falconer, M. K. Georgoulis, M. S. Wheatland, C. Balch, T. Dunn, E. L. Wagner
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 events have long been recognized in the meteorology community, but have yet to be fully acknowledged by the space weather community. During the interagency workshop on "all clear" forecasts held in Boulder, CO in 2009, the performance of a number of existing algorithms was compared on common data sets, specifically line-of-sight magnetic field and continuum intensity images from MDI, with consistent definitions of what constitutes an event. We demonstrate the importance of making such systematic comparisons, and of using standard verification statistics to determine what constitutes a good prediction scheme. When a comparison was made in this fashion, no one method clearly outperformed all others, which may in part be due to the strong correlations among the parameters used by different methods to characterize an active region. For M-class flares and above, the set of methods tends towards a weakly positive skill score (as measured with several distinct metrics), with no participating method proving substantially better than climatological forecasts.
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…
A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-Day Forecasting Patterns
成功预测耀斑的一个关键挑战是预测“耀斑平静”和“耀斑活跃”之间的过渡期。在本系列早期研究的基础上（Barnes et al. 2016; Leka et al. 2019 a，b），我们描述了耀斑预测比较工作的方法，细节和结果，我们在这里重点关注多日期间的预测结果（成功和失败）模式。一种新的分析开发，以评估预测成功的情况下，捕捉的第一个事件的耀斑活跃期，反之亦然，正确预测下降耀斑活动。我们以图形和定量的方式展示了这些评估方法，因为它们提供了快速的比较评估和详细分析的选项。对于2016-2017年的测试间隔，我们确定了三个不同事件历史的两天二分预报结果的相对频率分布（即，事件/事件、无事件/事件和事件/无事件），并使用它来突出预测方法之间的性能差异。在所有预测方法中确定了一种趋势，即第1天的高/低预测概率在第2天保持高/低，即使燃烧活动正在过渡。对于M级和更大的耀斑，我们发现，明确包括持久性或以前的耀斑历史计算预测有助于提高整体预测性能。还发现，使用磁性/现代数据可以改善捕捉第一事件/第一无事件转变的能力。最后，15%的主要（即，M级或以上）的耀斑天在测试间隔有效地错过了由于缺乏观测仪器远离地球-太阳线。
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 …
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 …
Solar flare forecasting from magnetic feature properties generated by Solar Monitor Active Region Tracker
我们研究了由太阳监测活动区跟踪器（SMART）生成的用于太阳耀斑预测的磁特征属性（MF）的预测能力，该预测能力来自两个数据集：Ahmed et al.（2011）先前研究的1996年至2010年SMART探测的完整数据集和仅包括NOAA活动区（AR）探测的数据集子集。主要贡献：我们使用边缘相关性作为过滤器特征选择方法来识别最有用的SMART MF属性，用于将燃烧与非燃烧检测分离，并使用逻辑回归来导出分类规则以预测未来的观测。为了进行比较，我们采用了随机森林，支持向量机和一组深度神经网络模型，以及Lasso进行特征选择。使用三个功能的线性模型，我们获得了显着更好的结果（TSS=0.84），艾哈迈德等人报道。（2011）的SMART检测的完整数据集。同样的模型产生了竞争力的结果（TSS=0.67）的SMART检测数据集，NOAA AR，可以比较更广泛的一部分耀斑预测文献。我们表明，更复杂的模型是不需要这些数据。
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.,…
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.
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…
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 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 Comparison of Flare Forecasting Methods. III. Systematic Behaviors of Operational Solar Flare Forecasting Systems
最近在日本名古屋大学空间-地球环境研究所举办了一个由国际合作研究中心赞助的研讨会（2017年10月31日至11月2日），以定量比较当今运行的太阳耀斑预测设施的性能。在本系列论文I（Barnes et al. 2016）的基础上，在论文II（Leka et al. 2019）中，我们描述了这项最新比较工作的参与方法，评估方法，并提出了定量比较。在本文中，我们专注于广泛的实施差异的背景下进行评估的方法的行为和性能。承认短的测试间隔和少量的方法可用，我们确实发现预测性能：1）通过包括持久性或先前的耀斑活动，区域演变和人类“循环中的预报员”来改善; 2）通过将数据限制在盘中心观测值而受到伤害; 3）可能受益于长期统计，但主要是在与现代数据来源和统计方法相结合时。这些趋势可以说是微弱的，必须在许多警告的情况下看待，正如本文和论文II所讨论的那样。根据目前的工作，我们在论文IV中提出了一种新的分析方法来评估这两种类型的预测误差的时间模式（即，遗漏和误报; Park et al. 2019）。因此，最重要的是，这一系列的论文，我们展示了技术，以促进比较，建立性能积极的方法。
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）
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…
Solar flares are a fundamental component of solar eruptive events (SEEs; along with solar energetic particles, SEPs, and coronal mass ejections, CMEs). Flares are the first component of the SEE to impact our atmosphere, which can set the stage for the arrival of the associated SEPs and CME. Magnetic reconnection drives SEEs by restructuring the solar coronal magnetic field, liberating a tremendous amount of energy which is partitioned into various physical manifestations: particle acceleration, mass and magnetic-field eruption, atmospheric heating, and the subsequent emission of radiation as s…
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^{…
