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It is widely known that the fast solar wind originates mainly from coronal holes (CHs) in the solar corona. Associations between the CH characteristics and the properties of the fast solar wind in situ have been studied throughout the years from different authors leading to diverse degrees of correlation (Nolte et al. 1976; Vršnak et al. 2007; Karachik et al. 2011; Rotter et al. 2012a; Hofmeister et al. 2018; Heinemann et al. 2020). In this work, we introduce and quantify the geometrical complexity of CHs, a parameter that has been neglected so far in similar studies. For a particular CH sample, we explore how complexity affects the peak speed of the fast solar wind at Earth and its association with other CH properties. We further compare observations of fast solar wind at Earth with forecasts from EUHFORIA. We evaluate our results, and present the efforts and restrictions we encounter towards improving our prediction capabilities by exploiting recent PSP observations.

Solar Energetic Particles (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to imific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.

We report on progress made by our Data Mining Lab at Georgia State University on the interdisciplinary research project funded by the NSF-OAC-1931555 award. We take on two main challenges hindering data-driven solar flare forecasting, which are: (1) extreme class imbalance among solar transient events, and (2) noisy “no-flare” labeling of continuous space weather data., NSF Award # 1931555. Elements: Comprehensive Time Series Data Analytics for the Prediction of Solar Flares and Eruptions

It has long been known that the high-speed stream (HSS) peak velocity at Earth directly depends on the area of the coronal hole (CH) on the Sun. Different degrees of association between the two parameters have been shown by many authors. In this study, we revisit this association in greater detail for a sample of 45 nonpolar CHs during the minimum phase of solar cycle 24. The aim is to understand how CHs of different properties influence the HSS peak speeds observed at Earth and draw from this to improve solar wind modeling. The characteristics of the CHs of our sample were extracted based on the Collection of Analysis Tools for Coronal Holes (CATCH) which employs an intensity threshold technique applied to extreme-ultraviolet (EUV) filtergrams. We first examined all the correlations between the geometric characteristics of the CHs and the HSS peak speed and duration at Earth, for the entire sample. The CHs were then categorized in different groups based on morphological criteria, such as the aspect ratio, the orientation angle and the geometric complexity, a parameter which is often neglected when the formation of the fast solar wind at Earth is studied. Our results, confirmed also by the bootstrapping technique, show that all three aforementioned morphological criteria play a major role in determining the HSS peak speed at 1 AU. Therefore, they need to be taken into consideration for empirical models that aim to forecast the fast solar wind at Earth based on the observed CH solar sources., Accepted by the Astronomy & Astrophysics journal

We present a catalog of solar energetic particle (SEP) events covering solar cycles 22, 23 and 24. We correlate and integrate three existing catalogs based on Geostationary Operational Environmental Satellite integral proton flux data. We visually verified and labeled each event in the catalog to provide a homogenized data set. We have identified a total of 341 SEP events of which 245 cross the Space Weather Prediction Center threshold of a significant proton event. The metadata consists of physical parameters and observables concerning the possible source solar eruptions, namely flares and coronal mass ejections for each event. The sliced time-series data of each event, along with intensity profiles of proton fluxes in several energy bands, have been made publicly available. This data set enables researchers in machine learning and statistical analysis to understand the SEPs and the source eruption characteristics useful for space weather prediction.

We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either “horizontally” – by adding more prediction-specific parameters, or “vertically” – by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future., Measurement(s) solar flare • stellar radiation • solar magnetic data Technology Type(s) digital curation Factor Type(s) temporal interval • flare class • location Sample Characteristic - Environment star • climate system Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12444884

In this ISSI-supported series of studies on magnetic helicity in the Sun, we systematically implement different magnetic helicity calculation methods on high-quality solar magnetogram observations. We apply finite-volume, discrete flux tube (in particular, connectivity-based) and flux-integration methods to data from Hinode's Solar Optical Telescope. The target is NOAA active region 10930 during a ~1.5 day interval in December 2006 that included a major eruptive flare (SOL2006-12-13T02:14X3.4). Finite-volume and connectivity-based methods yield instantaneous budgets of the coronal magnetic helicity, while the flux-integration methods allow an estimate of the accumulated helicity injected through the photosphere. The objectives of our work are twofold: A cross-validation of methods, as well as an interpretation of the complex events leading to the eruption. To the first objective, we find (i) strong agreement among the finite-volume methods, (ii) a moderate agreement between the connectivity-based and finite-volume methods, (iii) an excellent agreement between the flux-integration methods, and (iv) an overall agreement between finite-volume and flux-integration based estimates regarding the predominant sign and magnitude of the helicity. To the second objective, we are confident that the photospheric helicity flux significantly contributed to the coronal helicity budget, and that a right-handed structure erupted from a predominantly left-handed corona during the X-class flare. Overall, we find that the use of different methods to estimate the (accumulated) coronal helicity may be necessary in order to draw a complete picture of an active-region corona, given the careful handling of identified data (preparation) issues, which otherwise would mislead the event analysis and interpretation., Accepted for publication in The Astrophysical Journal, 29 pages, 9 figures

In this work an improved approach of existing approximations on the coupling function between primary and ground-level cosmic-ray particles is presented. The proposed coupling function is analytically derived based on a formalism used in Quantum Field Theory calculations. It is upgraded compared to previous versions with the inclusion of a wider energy spectrum that is extended to lower energies, as well as an altitude correction factor, also derived analytically. The improved approximations are applied to two cases of Forbush decreases detected in March 2012 and September 2017. In the analytical procedure for the derivation of the primary cosmic-ray spectrum during these events, we also consider the energy spectrum exponent $\gamma $ to be varied with time. For the validation of the findings, we present a direct comparison between the primary spectrum and the amplitude values derived by the proposed method and the obtained time series of the cosmic-ray intensity at the rigidity of 10 GV obtained from the Global Survey Method. The two sets of results are found to be in very good agreement for both events as denoted by the Pearson correlation factors and slope values of their scatter plots. In such way we determine the validity and applicability of our method to Forbush decreases as well as to other cosmic-ray phenomena, thus introducing a new, alternative way of inferring the primary cosmic-ray intensity.

An all-clear flare prediction is a type of solar flare forecasting that puts more emphasis on predicting non-flaring instances (often relatively small flares and flare quiet regions) with high precision while still maintaining valuable predictive results. While many flare prediction studies do not address this problem directly, all-clear predictions can be useful in operational context. However, in all-clear predictions, finding the right balance between avoiding false negatives (misses) and reducing the false positives (false alarms) is often challenging. Our study focuses on training and testing a set of interval-based time series classifiers named Time Series Forest (TSF). These classifiers will be used towards building an all-clear flare prediction system by utilizing multivariate time series data. Throughout this paper, we demonstrate our data collection, predictive model building and evaluation processes, and compare our time series classification models with baselines using our benchmark datasets. Our results show that time series classifiers provide better forecasting results in terms of skill scores, precision and recall metrics, and they can be further improved for more precise all-clear forecasts by tuning model hyperparameters.

With more than 4,300 confirmed exoplanets and counting, the next milestone in exoplanet research is to determine which of these newly found worlds could harbor life. Coronal Mass Ejections (CMEs), spawn by magnetically active, superflare-triggering dwarf stars, pose a direct threat to the habitability of terrestrial exoplanets as they can deprive them from their atmospheres. Here we develop a readily implementable atmosphere sustainability constraint for terrestrial exoplanets orbiting active dwarfs, relying on the magnetospheric compression caused by CME impacts. Our constraint focuses on a systems understanding of CMEs in our own heliosphere that, applying to a given exoplanet, requires as key input the observed bolometric energy of flares emitted by its host star. Application of our constraint to six famous exoplanets, (Kepler-438b, Proxima-Centauri b, and Trappist-1d, -1e, -1f and -1g), within or in the immediate proximity of their stellar host's habitable zones, showed that only for Kepler-438b might atmospheric sustainability against stellar CMEs be likely. This seems to align with some recent studies that, however, may require far more demanding computational resources and observational inputs. Our physically intuitive constraint can be readily and en masse applied, as is or generalized, to large-scale exoplanet surveys to detect planets that could be sieved for atmospheres and, perhaps, possible biosignatures at higher priority by current and future instrumentation., Comment: Accepted for publication by ApJ Letters

The onset and rapid advance of the Digital Age have brought challenges and opportunities for scientific research characterized by a continuously evolving data landscape reflected in the four V’s of big data: volume, variety, veracity, and velocity. The big data landscape supersedes traditional means of storage, processing, management, and exploration, and requires adaptation and innovation across the full data lifecycle (i.e., collection, storage and processing, analytics, and representation). The Topical Issue, “Space Weather research in the Digital Age and across the full data lifecycle”, collects research from across the full data lifecycle (collection, management, analysis, and communication; collectively “Data Science”) and offers a tractable compendium that illustrates the latest computational and data science trends, tools, and advances for Space Weather research. We introduce the paradigm shift in Space Weather and the articles in the Topical Issue. We create a network view of the research that highlights the contribution to the change of paradigm and reveals the trends that will guide it hereafter.

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 oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple ML algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic., Comment: 67 pages, 14 figures; submitted

Image super-resolution is a branch of image processing that is concerned with enhancing the spatial resolution and quality of images by learning the intrinsic details and relations between the lower resolution input and the higher resolution output images. It is widely accepted as an ill-posed problem, which has seen tremendous advancements with deep learning-based models. In this work, we present two super resolution models, Sub-Pixel Convolutional Neural Network (CNN) and Enhanced Deep Residual Networks (ResNet), which can be used for improving the spatial resolution of solar magnetograms. While the ill-posed nature of problem is still a challenge, there are several application areas, including space weather prediction, which can greatly benefit from the improved spatial resolution of solar magnetograms. Along with classical raster inputs we try to improve the model objective by giving HMI Active Region Patches. We show that through our experimental evaluation our models perform better than baselines and CNN-based super resolution model provides viable results for magnetogram super resolution.

Magnetic polarity inversion line (PIL) in solar active regions have been recognized as essential features for the occurrence of solar flares and the prediction of the flaring phenomenon. In this work, we provide a software framework that detects PILs from the line-of-sight (LoS) or the radial component of the magnetic field vector in active region magnetogram patches. The PIL detection procedure is based on an edge detection technique along with magnetic field strength and PIL size filter. First, we identify positive and negative polarity regions with a magnetic field strength threshold. Then, we utilize the Canny edge detector and morphological operations to both positive and negative regions to identify coarse PILs. Finally, we generate PILs by applying magnetic field strength and PIL size filter to the coarse PILs as mentioned above. Moreover, we provide feature extraction functions to obtain the properties of PILs (i.e., PIL size, the area of polarity inversion, the masked unsigned flux of enclosing PIL, convexity, eigenvalues, fractal dimension, and Hu moments of PIL shape), and produce three PIL-related binary masks (i.e., PIL, the region of polarity inversion, and the convex hull of PIL) for each Longitudinal magnetogram patch. We also provide a qualitative evaluation of our module. The detection results for a well known active region (NOAA AR 11158) are shown as proof of concept.

Image super-resolution is a branch of image processing that is concerned with enhancing the spatial resolution and quality of images by learning the intrinsic details and relations between the lower resolution input and the higher resolution output images. It is widely accepted as an ill-posed problem, which has seen tremendous advancements with deep learning based models. In this work, we present two magnetogram super resolution models, Sub-Pixel Convolutional Neural Network (CNN) and Enhanced Deep Residual Networks (ResNet), which can be used for improving the spatial resolution of solar magnetograms. While the ill-posed nature of problem is still a challenge, there are several application areas, including space weather prediction, which can greatly benefit from the improved spatial resolution of solar magnetograms. We show that through our experimental evaluation our models perform better than baselines and Sub-Pixel CNN super resolution model provides viable results for magnetogram super resolution.

A clear understanding of the nature of the pre-eruptive magnetic field configurations of Coronal Mass Ejections (CMEs) is required for understanding and eventually predicting solar eruptions. Only two, but seemingly disparate, magnetic configurations are considered viable; namely, sheared magnetic arcades (SMA) and magnetic flux ropes (MFR). They can form via three physical mechanisms (flux emergence, flux cancellation, helicity condensation) . Whether the CME culprit is an SMA or an MFR, however, has been strongly debated for thirty years. We formed an International Space Science Institute (ISSI) team to address and resolve this issue and report the outcome here. We review the status of the field across modeling and observations, identify the open and closed issues, compile lists of SMA and MFR observables to be tested against observations and outline research activities to close the gaps in our current understanding. We propose that the combination of multi-viewpoint multi-thermal coronal observations and multi-height vector magnetic field measurements is the optimal approach for resolving the issue conclusively. We demonstrate the approach using MHD simulations and synthetic coronal images. Our key conclusion is that the differentiation of pre-eruptive configurations in terms of SMAs and MFRs seems artificial. Both observations and modeling can be made consistent if the pre-eruptive configuration exists in a hybrid state that is continuously evolving from an SMA to an MFR. Thus, the 'dominant' nature of a given configuration will largely depend on its evolutionary stage (SMA-like early-on, MFR-like near the eruption)., Space Science Reviews, accepted for publication

All authors: Rouillard, A. P.; Pinto, R. F.; Vourlidas, A.; De Groof, A.; Thompson, W. T.; Bemporad, A.; Dolei, S.; Indurain, M.; Buchlin, E.; Sasso, C.; Spadaro, D.; Dalmasse, K.; Hirzberger, J.; Zouganelis, I.; Strugarek, A.; Brun, A. S.; Alexandre, M.; Berghmans, D.; Raouafi, N. E.; Wiegelmann, T.; Pagano, P.; Arge, C. N.; Nieves-Chinchilla, T.; Lavarra, M.; Poirier, N.; Amari, T.; Aran, A.; Andretta, V.; Antonucci, E.; Anastasiadis, A.; Auchère, F.; Bellot Rubio, L.; Nicula, B.; Bonnin, X.; Bouchemit, M.; Budnik, E.; Caminade, S.; Cecconi, B.; Carlyle, J.; Cernuda, I.; Davila, J. M.; Etesi, L.; Espinosa Lara, F.; Fedorov, A.; Fineschi, S.; Fludra, A.; Génot, V.; Georgoulis, M. K.; Gilbert, H. R.; Giunta, A.; Gomez-Herrero, R.; Guest, S.; Haberreiter, M.; Hassler, D.; Henney, C. J.; Howard, R. A.; Horbury, T. S.; Janvier, M.; Jones, S. I.; Kozarev, K.; Kraaikamp, E.; Kouloumvakos, A.; Krucker, S.; Lagg, A.; Linker, J.; Lavraud, B.; Louarn, P.; Maksimovic, M.; Maloney, S.; Mann, G.; Masson, A.; Müller, D.; Önel, H.; Osuna, P.; Orozco Suarez, D.; Owen, C. J.; Papaioannou, A.; Pérez-Suárez, D.; Rodriguez-Pacheco, J.; Parenti, S.; Pariat, E.; Peter, H.; Plunkett, S.; Pomoell, J.; Raines, J. M.; Riethmüller, T. L.; Rich, N.; Rodriguez, L.; Romoli, M.; Sanchez, L.; Solanki, S. K.; St Cyr, O. C.; Straus, T.; Susino, R.; Teriaca, L.; del Toro Iniesta, J. C.; Ventura, R.; Verbeeck, C.; Vilmer, N.; Warmuth, A.; Walsh, A. P.; Watson, C.; Williams, D.; Wu, Y.; Zhukov, A. N.-- Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited., Context. The Solar Orbiter spacecraft will be equipped with a wide range of remote-sensing (RS) and in situ (IS) instruments to record novel and unprecedented measurements of the solar atmosphere and the inner heliosphere. To take full advantage of these new datasets, tools and techniques must be developed to ease multi-instrument and multi-spacecraft studies. In particular the currently inaccessible low solar corona below two solar radii can only be observed remotely. Furthermore techniques must be used to retrieve coronal plasma properties in time and in three dimensional (3D) space. Solar Orbiter will run complex observation campaigns that provide interesting opportunities to maximise the likelihood of linking IS data to their source region near the Sun. Several RS instruments can be directed to specific targets situated on the solar disk just days before data acquisition. To compare IS and RS, data we must improve our understanding of how heliospheric probes magnetically connect to the solar disk. Aims. The aim of the present paper is to briefly review how the current modelling of the Sun and its atmosphere can support Solar Orbiter science. We describe the results of a community-led effort by European Space Agency's Modelling and Data Analysis Working Group (MADAWG) to develop different models, tools, and techniques deemed necessary to test different theories for the physical processes that may occur in the solar plasma. The focus here is on the large scales and little is described with regards to kinetic processes. To exploit future IS and RS data fully, many techniques have been adapted to model the evolving 3D solar magneto-plasma from the solar interior to the solar wind. A particular focus in the paper is placed on techniques that can estimate how Solar Orbiter will connect magnetically through the complex coronal magnetic fields to various photospheric and coronal features in support of spacecraft operations and future scientific studies. Methods. Recent missions such as STEREO, provided great opportunities for RS, IS, and multi-spacecraft studies. We summarise the achievements and highlight the challenges faced during these investigations, many of which motivated the Solar Orbiter mission. We present the new tools and techniques developed by the MADAWG to support the science operations and the analysis of the data from the many instruments on Solar Orbiter. Results. This article reviews current modelling and tool developments that ease the comparison of model results with RS and IS data made available by current and upcoming missions. It also describes the modelling strategy to support the science operations and subsequent exploitation of Solar Orbiter data in order to maximise the scientific output of the mission. Conclusions. The on-going community effort presented in this paper has provided new models and tools necessary to support mission operations as well as the science exploitation of the Solar Orbiter data. The tools and techniques will no doubt evolve significantly as we refine our procedure and methodology during the first year of operations of this highly promising mission. © 2020 A. P. Rouillard et al., Solar Orbiter is a joint ESA and NASA mission. A. Vourlidas' Solar Orbiter effort is supported by NRL grant N00173-16-1-G029. P. Pagano would like to thank D. H. Mackay and S. L. Yardley for their valuable contributions, the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (grant agreement No. 647214), and the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (http://www.dirac.ac.uk).This equipment was funded by a BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/K00087X/1, DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure. A. Rouillard, V. Genot, M. Janvier, Elie Soubrier, F. Auchere, E. Buchlin and E. Pariat acknowledge support from the French space agency (Centre National d'Etudes Spatiales; CNES; https://cnes.fr/fr) that funds activity in plasma physics data center (Centre de Donnees de la Physique des Plasmas; CDPP; http://cdpp.eu/) and the Multi Experiment Data and Operation Center (MEDOC; https://idoc.ias.u-psud.fr/MEDOC), and the space weather team in Toulouse (Solar-Terrestrial Observations and Modelling Service; STORMS; http://storms-service.irap.omp.eu/).This includes funding for Gaia-DEM, the data mining tools AMDA (http://amda.cdpp.eu/), CLWEB (clweb.cesr. fr/) and the propagation tool (http://propagationtool.cdpp.eu).R.Pinto, M. Lavarra, Y. Wu and A. Kouloumvakos acknowledge financial support from the ANR project SLOW_ SOURCE No. ANR-17-CE31-0006-01, ANR project COROSHOCK No. ANR-18-ERC1-0006-01 and FP7 HELCATS project https://www.helcats-fp7.eu/under the FP7 EU contract number 606692. A. Warmuth acknowledges the support by DLR under grant No. 50 QL 0001. The STEREO SECCHI data are produced by a consortium of RAL (UK), NRL (USA), LMSAL (USA), GSFC (USA), MPS (Germany), CSL (Belgium), IOTA (France) and IAS (France). The ACE data were obtained from the ACE science center. The WIND data were obtained from the Space Physics Data Facility. Javier Rodriguez-Pacheco acknowledges Spanish Project: FEDER/MCIU-AEI/Project ESP2017-88436-R.

All authors: Zouganelis, I.; De Groof, A.; Walsh, A. P.; Williams, D. R.; Müller, D.; St Cyr, O. C.; Auchère, F.; Berghmans, D.; Fludra, A.; Horbury, T. S.; Howard, R. A.; Krucker, S.; Maksimovic, M.; Owen, C. J.; Rodríguez-Pacheco, J.; Romoli, M.; Solanki, S. K.; Watson, C.; Sanchez, L.; Lefort, J.; Osuna, P.; Gilbert, H. R.; Nieves-Chinchilla, T.; Abbo, L.; Alexandrova, O.; Anastasiadis, A.; Andretta, V.; Antonucci, E.; Appourchaux, T.; Aran, A.; Arge, C. N.; Aulanier, G.; Baker, D.; Bale, S. D.; Battaglia, M.; Bellot Rubio, L.; Bemporad, A.; Berthomier, M.; Bocchialini, K.; Bonnin, X.; Brun, A. S.; Bruno, R.; Buchlin, E.; Büchner, J.; Bucik, R.; Carcaboso, F.; Carr, R.; Carrasco-Blázquez, I.; Cecconi, B.; Cernuda Cangas, I.; Chen, C. H. K.; Chitta, L. P.; Chust, T.; Dalmasse, K.; D'Amicis, R.; Da Deppo, V.; De Marco, R.; Dolei, S.; Dolla, L.; Dudok de Wit, T.; van Driel-Gesztelyi, L.; Eastwood, J. P.; Espinosa Lara, F.; Etesi, L.; Fedorov, A.; Félix-Redondo, F.; Fineschi, S.; Fleck, B.; Fontaine, D.; Fox, N. J.; Gandorfer, A.; Génot, V.; Georgoulis, M. K.; Gissot, S.; Giunta, A.; Gizon, L.; Gómez-Herrero, R.; Gontikakis, C.; Graham, G.; Green, L.; Grundy, T.; Haberreiter, M.; Harra, L. K.; Hassler, D. M.; Hirzberger, J.; Ho, G. C.; Hurford, G.; Innes, D.; Issautier, K.; James, A. W.; Janitzek, N.; Janvier, M.; Jeffrey, N.; Jenkins, J.; Khotyaintsev, Y.; Klein, K. -L.; Kontar, E. P.; Kontogiannis, I.; Krafft, C.; Krasnoselskikh, V.; Kretzschmar, M.; Labrosse, N.; Lagg, A.; Landini, F.; Lavraud, B.; Leon, I.; Lepri, S. T.; Lewis, G. R.; Liewer, P.; Linker, J.; Livi, S.; Long, D. M.; Louarn, P.; Malandraki, O.; Maloney, S.; Martinez-Pillet, V.; Martinovic, M.; Masson, A.; Matthews, S.; Matteini, L.; Meyer-Vernet, N.; Moraitis, K.; Morton, R. J.; Musset, S.; Nicolaou, G.; Nindos, A.; O'Brien, H.; Orozco Suarez, D.; Owens, M.; Pancrazzi, M.; Papaioannou, A.; Parenti, S.; Pariat, E.; Patsourakos, S.; Perrone, D.; Peter, H.; Pinto, R. F.; Plainaki, C.; Plettemeier, D.; Plunkett, S. P.; Raines, J. M.; Raouafi, N.; Reid, H.; Retino, A.; Rezeau, L.; Rochus, P.; Rodriguez, L.; Rodriguez-Garcia, L.; Roth, M.; Rouillard, A. P.; Sahraoui, F.; Sasso, C.; Schou, J.; Schühle, U.; Sorriso-Valvo, L.; Soucek, J.; Spadaro, D.; Stangalini, M.; Stansby, D.; Steller, M.; Strugarek, A.; Štverák, Š.; Susino, R.; Telloni, D.; Terasa, C.; Teriaca, L.; Toledo-Redondo, S.; del Toro Iniesta, J. C.; Tsiropoula, G.; Tsounis, A.; Tziotziou, K.; Valentini, F.; Vaivads, A.; Vecchio, A.; Velli, M.; Verbeeck, C.; Verdini, A.; Verscharen, D.; Vilmer, N.; Vourlidas, A.; Wicks, R.; Wimmer-Schweingruber, R. F.; Wiegelmann, T.; Young, P. R.; Zhukov, A. N., Solar Orbiter is the first space mission observing the solar plasma both in situ and remotely, from a close distance, in and out of the ecliptic. The ultimate goal is to understand how the Sun produces and controls the heliosphere, filling the Solar System and driving the planetary environments. With six remote-sensing and four in-situ instrument suites, the coordination and planning of the operations are essential to address the following four top-level science questions: (1) What drives the solar wind and where does the coronal magnetic field originate?; (2) How do solar transients drive heliospheric variability?; (3) How do solar eruptions produce energetic particle radiation that fills the heliosphere?; (4) How does the solar dynamo work and drive connections between the Sun and the heliosphere? Maximising the mission's science return requires considering the characteristics of each orbit, including the relative position of the spacecraft to Earth (affecting downlink rates), trajectory events (such as gravitational assist manoeuvres), and the phase of the solar activity cycle. Furthermore, since each orbit's science telemetry will be downloaded over the course of the following orbit, science operations must be planned at mission level, rather than at the level of individual orbits. It is important to explore the way in which those science questions are translated into an actual plan of observations that fits into the mission, thus ensuring that no opportunities are missed. First, the overarching goals are broken down into specific, answerable questions along with the required observations and the so-called Science Activity Plan (SAP) is developed to achieve this. The SAP groups objectives that require similar observations into Solar Orbiter Observing Plans, resulting in a strategic, top-level view of the optimal opportunities for science observations during the mission lifetime. This allows for all four mission goals to be addressed. In this paper, we introduce Solar Orbiter's SAP through a series of examples and the strategy being followed. © 2020 ESO., Solar Orbiter is a space mission of international collaboration between ESA and NASA with contributions from national agencies of ESA member states. The spacecraft has been developed by Airbus and is being operated by ESA from the European Space Operations Centre (ESOC) in Darmstadt, Germany. Science operations are carried out at ESA's European Space Astronomy Centre (ESAC) in Villafranca del Castillo, Spain. SWA is an international collaboration which has been funded by the UKSA, CNES, ASI, NASA and the Czech contribution to the ESA PRODEX programme. UAH authors want to thanks the Spanish MINECO-FPI-2016 predoctoral grant with FSE, and its project FEDER/MCIU-AEEI/Proyecto ESP2017-88436-R. The Spanish contribution to SO/PHI has been funded by the Spanish Ministry of Science and Innovation through several projects, the last one being RTI2018-096886-B-C5, and by "Centro de Excelencia Severo Ochoa" programme under grant SEV-2017-0709. RAH, RCC, DMM, SPP, and AV acknowledge the support of the NASA Heliophysics Division, Solar Orbiter Collaboration Office under IAT NNG09EK11I. JEP acknowledges grant UKRI/STFC ST/N000692/1. All French involvements are supported by CNES and CNRS. DV is supported by the STFC Ernest Rutherford Fellowship ST/P003826/1 and STFC Consolidated Grant ST/S000240/1. The authors thank the referee for her constructive comments and suggestions, which led to the substantial improvement of this paper.

In this article, we present the application of the weighted horizontal gradient of magnetic field (WG_M) flare prediction method to 3-dimensional (3D) extrapolated magnetic configurations of 13 flaring solar active regions (ARs). The main aim is to identify an optimal height range, if any, in the interface region between the photosphere and lower corona, where the flare onset time prediction capability of WGM is best exploited. The optimal height is where flare prediction, by means of the WG_M method, is achieved earlier than at the photospheric level. 3D magnetic structures, based on potential and non-linear force-free field extrapolations, are constructed to study a vertical range from the photosphere up to the low corona with a 45 km step size. The WG_M method is applied as a function of height to all 13 flaring AR cases that are subject to certain selection criteria. We found that applying the WGM method between 1000 and 1800 km above the solar surface would improve the prediction of the flare onset time by around 2-8 hours.Certain caveats and an outlook for future work along these lines are also discussed. ispartof: Astrophysical Journal vol:896 issue:2 status: published

We present a case study for time series prediction models in extreme class-imbalance problems. We have extracted multiple properties from the Space Weather ANalytics for Solar Flares (SWAN-SF) benchmark dataset which comprises of magnetic features from over 4075 active regions over a period of 9 years to create the forecasting dataset used in this study. In the extracted dataset, the class-imbalance ratio is 1:60, where the minority class is formed by instances of strong solar flares (GOES M-and X-class). This ratio reaches to 1:800 if we only consider the strongest class of flares (GOES X-class). This case of extreme imbalance, along with the temporal coherence of the sliced time series, provides us with an interesting set of challenges in the forecasting of scarce real-life phenomena. We have explored remedies to tackle the class-imbalance issue such as undersampling, oversampling and misclassification weights. In the process, we elaborate on common mistakes and pitfalls caused by ignoring the side effects of these remedies, including how and why they weaken the robustness of the trained models while seemingly improving the performance.

Spatio-temporal co-occurrence analysis captures the spatial and temporal relations between events that occur at the same time and location. In this paper, we utilize spatio-temporal co-occurrence relations to integrate solar active region (AR) data detected and reported by three feature recognition methods, namely, human labeling by forecasters at National Oceanic and Atmospheric Administration (NOAA), Spaceweather HMI Active Region Patch (SHARP) detection pipeline, and Spatial Possibilistic Clustering Algorithm (SPoCA). We determine the associations between individual reports by identifying the spatio-temporal co-occurrences among the reports from these modules. We compare our findings with the data from the Joint Science Operations Center (JSOC), analyzing the discrepancies in different circumstances. We found 105 SHARP series not properly associated with the NOAA-labeled ARs. In the end, we provide detailed movement analyses for the AR trajectories, create an updated SHARP-to-NOAA AR associations, that is crucial for space weather predictions utilizing magnetic field information, and make the ternary associations between SHARP, NOAA ARs, and SPoCA ARs available to the public.

Space weather encapsulates the impact of variable solar activity on the vicinity of Earth and elsewhere in the solar system. A major agent of space weather, with significant effort already devoted to its prediction, is solar flares. Most existing analysis in this direction focus on the instantaneous (point-in-time) magnitude of various pre-flare parameters in flare host locations, solar active regions. Nonetheless, a recent trend places data-intensive studies, focusing on the pre-flare time series of these parameters, to the forefront. We take on this task in this study, focusing on the shape of pre-flare active region parameter time series by introducing a data-driven class profiling and clustering of these time series. We rely on data provided by the Space Weather ANalytics for Solar Flares (SWAN-SF) benchmark dataset. Our results indicate some potentially interesting temporal patterns that are unrelated to parameter magnitudes and may be used, both in tandem and independently from magnitudes, for future flare forecasting efforts. Our analysis also provides flexibility to define custom flare classes relying on pre-flare time series behavior and relate them to the existing, conventional NOAA / GOES flare classes.

Machine learning-based space weather analytics has attracted much attention due to the potential damages that can be caused by the extreme space weather events. Using a recently released data benchmark, named SWAN-SF, designed for solar flare forecasting based on the pre-flare time series of solar magnetic field parameters, we conduct a case study on the impacts of statistical features derived from the multivariate time series. We investigate the relationship between the number of needed statistical features extracted from the multi-variate time series and the performance of flare forecast models. To that end, we employ random forest and mean decrease impurity to determine a feature selection methodology along with an evaluation procedure. The proposed evaluation method delivers a balance between the two frequently used metrics in this domain, namely True Skill Statistic and Heidke Skill Score. Our approach allows to introduce a generic feature selection and evaluation procedure that is independent from the minor and often obscured decisions that must be made for having a binary forecast model, while presenting interpretable and actionable tools that can help non-data experts make more informed and realistic decisions.

We investigate the relation between characteristics of coronal mass ejections and parameterizations of the eruptive capability of solar active regions widely used in solar flare prediction schemes. These parameters, some of which are explored for the first time, are properties related to topological features, namely, magnetic polarity inversion lines (MPILs) that indicate large amounts of stored non-potential (i.e. free) magnetic energy. We utilize the Space Weather Database of Notifications, Knowledge, Information (DONKI) and the Large Angle and Spectrometric Coronograph (LASCO) databases to find flare-associated coronal mass ejections and their kinematic characteristics while properties of MPILs are extracted from Helioseismic and Magnetic Imager (HMI) vector magnetic-field observations of active regions to extract the properties of source-region MPILs. The correlation between all properties and the characteristics of CMEs ranges from moderate to very strong. More significant correlations hold particularly for fast CMEs, which are most important in terms of adverse space-weather manifestations. Non-neutralized currents and the length of the main MPIL exhibit significantly stronger correlations than the rest of the properties. This finding supports a causal relationship between coronal mass ejections and non-neutralized electric currents in highly sheared, conspicuous MPILs. In addition, non-neutralized currents and MPIL length carry distinct, independent information as to the eruptive potential of active regions. The combined total amount of non-neutralized electric currents and the length of the main polarity inversion line, therefore, reflect more efficiently than other parameters the eruptive capacity of solar active regions and the CME kinematic characteristics stemming from these regions., 31 pages, 14 figures

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 behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human "forecaster in the loop"; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies., Comment: 23 pages, 6 figures, accepted for publication in The Astrophysical Journal

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 I), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to - for the first time - directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the "no skill" level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series we ask why the performances differ by examining implementation details (Leka et al. 2019, Paper III), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in (Park et al. 2019, Paper IV). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks, and today's performance benchmarks against which improvements and new methods may be compared., 26 pages, 5 figures, accepted for publication in the Astrophysical Journal Supplement Series

Coronal mass ejections (CMEs) are large-scale expulsions of coronal plasma and magnetic field propagating through the heliosphere. Because CMEs are observed by white-light coronagraphs which, by design, occult the solar disc, supporting disc observations (e.g. in EUV, soft X-rays, Halpha and radio) must be employed for the study of their source regions and early development phases. We review the key properties of CME sources and highlight a certain causal sequence of effects that may occur whenever a strong (flux-massive and sheared) magnetic polarity inversion line develops in the coronal base of eruptive active regions (ARs). Storing non-potential magnetic energy and helicity in a much more efficient way than ARs lacking strong polarity inversion lines, eruptive regions engage in an irreversible course, making eruptions inevitable and triggered when certain thresholds of free energy and helicity are crossed. This evolution favours the formation of pre-eruption magnetic flux ropes. We describe the steps of this plausible path to sketch a picture of the pre-eruptive phase of CMEs that may apply to most events, particularly the ones populating the high end of the energy/helicity distribution, that also tend to have the strongest space-weather implications.This article is part of the theme issue ‘Solar eruptions and their space weather impact’.

In analyses of rare-events, regardless of the domain of application, class-imbalance issue is intrinsic. Although the challenges are known to data experts, their explicit impact on the analytic and the decisions made based on the findings are often overlooked. This is in particular prevalent in interdisciplinary research where the theoretical aspects are sometimes overshadowed by the challenges of the application. To show-case these undesirable impacts, we conduct a series of experiments on a recently created benchmark data, named Space Weather ANalytics for Solar Flares (SWAN-SF). This is a multivariate time series dataset of magnetic parameters of active regions. As a remedy for the imbalance issue, we study the impact of data manipulation (undersampling and oversampling) and model manipulation (using class weights). Furthermore, we bring to focus the auto-correlation of time series that is inherited from the use of sliding window for monitoring flares' history. Temporal coherence, as we call this phenomenon, invalidates the randomness assumption, thus impacting all sampling practices including different cross-validation techniques. We illustrate how failing to notice this concept could give an artificial boost in the forecast performance and result in misleading findings. Throughout this study we utilized Support Vector Machine as a classifier, and True Skill Statistics as a verification metric for comparison of experiments. We conclude our work by specifying the correct practice in each case, and we hope that this study could benefit researchers in other domains where time series of rare events are of interest., Comment: 9 pages, 9 figures, and 1 table, accepted in IEEE BigData 2019

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 course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine learning methods that allow feature ranking as a function of predictive capability, we show that: i) an objective training and testing process is paramount for the performance of every supervised machine learning method; ii) most properties include overlapping information and are therefore highly redundant for flare prediction; iii) solar flare prediction is still - and will likely remain - a predominantly probabilistic challenge.

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. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event and event/no-event), and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day-1 remains high/low on day-2 even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line., Comment: 33 pages, 13 figures, accepted for publication in ApJ