1. The flare likelihood and region eruption forecasting (FLARECAST) project: Flare forecasting in the big data & machine learning era
- Author
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Marco Soldati, Michele Piana, Mark Worsfold, Constantinos Gontikakis, Manolis K. Georgoulis, Samuelvon von Stachelski, N. Vilmer, Chloé Guennou, André Csillaghy, Jordan A. Guerra, Cristina Campi, Eric Buchlin, Pablo Alingery, David Jackson, Sophie A. Murray, Aleksandar Torbica, Peter T. Gallagher, F. Baudin, Federico Benvenuto, Konstantinos Florios, D. Shaun Bloomfield, Sung-Hong Park, Anna Maria Massone, H. Sathiapal, Dario Vischi, Vittorio Latorre, Etienne Pariat, Ioannis Kontogiannis, Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA (UMR_8109)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut d'astrophysique spatiale (IAS), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,F300 ,Computer science ,Big data ,F500 ,Machine learning ,computer.software_genre ,7. Clean energy ,01 natural sciences ,law.invention ,Sun ,solar flares ,solar flare forecasting ,machine learning ,big data ,computer science ,law ,Meteorology. Climatology ,0103 physical sciences ,Coronal mass ejection ,media_common.cataloged_instance ,European union ,010303 astronomy & astrophysics ,0105 earth and related environmental sciences ,media_common ,Solar flare forecasting ,Solar flare ,business.industry ,Lift (data mining) ,[SDU.ASTR.SR]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR] ,Probabilistic logic ,Training (meteorology) ,Solar flares ,Astrophysics - Solar and Stellar Astrophysics ,13. Climate action ,Space and Planetary Science ,Artificial intelligence ,QC851-999 ,business ,computer ,Flare - Abstract
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
- Published
- 2021
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