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

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.

Validation of a Wave Heated 3D MHD Coronal-wind Model using Polarized Brightness and EUV Observations

“…For instance, this was recently done for solar eclipses by Mikić et al (2018). Other important forecast are solar eruptions (e.g., Leka & Barnes 2003;Leka et al 2019;Georgoulis et al 2021) with possible Earth impact depending on the connectivity, as well as the preparation of the Solar Orbiter remote sensing observations. Solar Orbiter (Müller et al 2020), launched in February 2020, has a mission profile where the remote sensing instruments (some of them with a limited field of view) are observing only during limited periods called "windows," some of which will cover the passage along the far side of the Sun.…”

Space Weather research in the Digital Age and across the full data lifecycle: Introduction to the Topical Issue

“…The Topical Issue also contains frontier scientific research obtained in synergy with data science methods. Georgoulis et al (2021) summarize the The Flare Likelihood and Region Eruption Forecasting (FLARECAST) Project. They give an account of progress and challenges in solar flare prediction within a diverse consortium that has made openly available all comprehensive data, codes and infrastructure spawned in the course of the project.…”

Convolutional Neural Networks for Searching Superflares from Pixel-level Data of the Transiting Exoplanet Survey Satellite

“…Zhang et al (2021) used random forest for classification of 4XMM-DR9 sources. Georgoulis et al (2021) used deep learning methods for solar flare forecasting. Unsupervised clustering method was used for classifying fast radio bursts (FRBs) (Chen et al 2022).…”