The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era

• Main Authors: 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, von Stachelski Samuel, Torbica Aleksandar, Vischi Dario, Worsfold Mark
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: Journal of Space Weather and Space Climate
• Subjects: sun; solar flares; solar flare forecasting; machine learning; big data; computer science
• Online Access: https://www.swsc-journal.org/articles/swsc/full_html/2021/01/swsc200032/swsc200032.html

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.