Agora – Project report

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

¹ RCAAM of the Academy of Athens, 11527 Athens, Greece
² Department of Mathematics, Physics & Electrical Engineering, Northumbria University, NE1 8ST Newcastle upon Tyne, UK
³ School of Physics, Trinity College Dublin, Dublin 2, Ireland
⁴ Dipartimento di Matematica, Università di Genova, Via Dodecaneso 35, 16146 Genova, Italy
⁵ CNR – SPIN Genova, Via Dodecaneso 33, 16146 Genova, Italy
⁶ University of Applied Sciences & Arts Northwestern Switzerland, 5210 Windisch, Switzerland
⁷ School of Cosmic Physics, Dublin Institute for Advanced Studies, D02 XF85 Dublin, Ireland
⁸ LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, 75014 Paris, France
⁹ Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405, Orsay, France
¹⁰ Met Office, EX1 3QS Exeter, UK
¹¹ Dipartimento di Matematica “Tullio Levi-Civita”, Università di Padova, 35131 Padova, Italy
¹² Department of Chemical Engineering, National Technical University of Athens, 15772 Zografou, Greece
¹³ Department of Physics, Villanova University, 800 E Lancaster Ave., Villanova, 19085 PA, USA
¹⁴ Leibniz-Institut für Astrophysik Potsdam (AIP), 14482 Potsdam, Germany
¹⁵ Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, 464-8601 Aichi, Japan

^* Corresponding author: manolis.georgoulis@academyofathens.gr

Received: 28 April 2020
Accepted: 26 May 2021

Abstract

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.