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search.filters.applied.f.access: openAccess × Start date: 2021 × DDC: 550 × Publisher: Les Ulis : EDP Sciences ×

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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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    Lensing from small-scale travelling ionospheric disturbances observed using LOFAR
    (Les Ulis : EDP Sciences, 2022) Boyde, Ben; Wood, Alan; Dorrian, Gareth; Fallows, Richard A.; Themens, David; Mielich, Jens; Elvidge, Sean; Mevius, Maaijke; Zucca, Pietro; Dabrowski, Bartosz; Krankowski, Andrzej; Vocks, Christian; Bisi, Mario
    Observations made using the LOw-Frequency ARray (LOFAR) between 10:15 and 11:48 UT on the 15th of September 2018 over a bandwidth of approximately 25-65 MHz contain discrete pseudo-periodic features of ionospheric origin. These features occur within a period of approximately 10 min and collectively last roughly an hour. They are strongly frequency dependent, broadening significantly in time towards the lower frequencies, and show an overlaid pattern of diffraction fringes. By modelling the ionosphere as a thin phase screen containing a wave-like disturbance, we are able to replicate the observations, suggesting that they are associated with small-scale travelling ionospheric disturbances (TIDs). This modelling indicates that the features observed here require a compact radio source at a low elevation and that the TID or TIDs in question have a wavelength <~30 km. Several features suggest the presence of deviations from an idealised sinusoidal wave form. These results demonstrate LOFAR-s capability to identify and characterise small-scale ionospheric structures.