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Author: Bloomfield, D. Shaun × Type: Article ×

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    The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era
    (Les Ulis : EDP Sciences, 2021) Georgoulis, Manolis K.; Bloomfield, D. Shaun; Piana, Michele; Massone, Anna Maria; Soldati, Marco; Gallagher, Peter T.; Pariat, Etienne; Vilmer, Nicole; Buchlin, Eric; Baudin, Frederic; Csillaghy, Andre; Sathiapal, Hanna; Jackson, David R.; Alingery, Pablo; Benvenuto, Federico; Campi, Cristina; Florios, Konstantinos; Gontikakis, Constantinos; Guennou, Chloe; Guerra, Jordan A.; Kontogiannis, Ioannis; Latorre, Vittorio; Murray, Sophie A.; Park, Sung-Hong; Stachelski, Samuel von; Torbica, Aleksandar; Vischi, Dario; Worsfold, Mark
    The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly 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 machine learning 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 machine-learning 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 least for major flares. 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.
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    Validation of Global EUV Wave MHD Simulations and Observational Techniques
    (London : Institute of Physics Publ., 2021) Downs, Cooper; Warmuth, Alexander; Long, David M.; Bloomfield, D. Shaun; Kwon, Ryun-Young; Veronig, Astrid M.; Vourlidas, Angelos; Vršnak, Bojan
    Global EUV waves remain a controversial phenomenon more than 20 yr after their discovery by SOHO/EIT. Although consensus is growing in the community that they are most likely large-amplitude waves or shocks, the wide variety of observations and techniques used to identify and analyze them have led to disagreements regarding their physical properties and interpretation. Here, we use a 3D magnetohydrodynamic (MHD) model of the solar corona to simulate an EUV wave event on 2009 February 13 to enable a detailed validation of the various commonly used detection and analysis techniques of global EUV waves. The simulated event exhibits comparable behavior to that of a real EUV wave event, with similar kinematic behavior and plasma parameter evolution. The kinematics of the wave are estimated via visual identification and profile analysis, with both approaches providing comparable results. We find that projection effects can affect the derived kinematics of the wave, due to the variation in fast-mode wave speed with height in the corona. Coronal seismology techniques typically used for estimates of the coronal magnetic field are also tested and found to estimate fast-mode speeds comparable to those of the model. Plasma density and temperature variations of the wave front are also derived using a regularized inversion approach and found to be consistent with observed wave events. These results indicate that global waves are best interpreted as large-amplitude waves and that they can be used to probe the coronal medium using welldefined analysis techniques.