Periodic Reporting for period 2 - FLARECAST (Flare Likelihood and Region Eruption Forecasting) Reporting period: 2016-01-01 to 2017-12-31 Summary of the context and overall objectives of the project TITLE: FLARECAST – Flare Likelihood and Region Eruption ForecastingFUNDING AGENCY: EC Horizon 2020, RIA, Protec-1-2014: Space WeatherGRANT AMOUNT: EUR 2,400 000.00DURATION: 3 yearsSTART DATE: 1 January 2015CONSORTIUM: 9 institutions from 6 countriesPROJECT COORDINATOR: Dr. Manolis K. Georgoulis, Academy of Athens, GreecePROJECT SCIENTIST: Dr. D. Shaun Bloomfield, Trinity College Dublin, IrelandPROJECT WEBSITE: flarecast.euKEY WORDS: Flare Forecasting, Space Weather, Heliophysics, Prediction Algorithms, Machine LearningTHE CHALLENGESpace weather can have detrimental effects on astronaut safety and a multitude of technologies on which we rely on a daily basis. Accurate and reliable space-weather monitoring and forecasting helps those affected, such as satellite operators, to take timely impact-mitigation measures. FLARECAST will significantly advance our ability to predict flares prior to their occurrence in the Sun, aiming to assist existing and future mitigation efforts. PROJECT OBJECTIVESFLARECAST will first aim to understand the drivers of solar-flare activity to improve flare prediction. It will then aim to provide a globally and openly accessible flare prediction service that facilitates evolution and expansion. Finally, FLARECAST will aim to engage in a dialogue with space-weather stakeholders, policy makers, and the public on the societal benefits of a reliable solar-flare prediction.METHODOLOGYSolar active-region properties will be extracted using advanced image-processing techniques applied to remote-sensing solar observations. Flare prediction algorithms, mostly relying on machine learning, will highlight a statistically rigorous set of the most promising of these predictors. Flare-forecast probabilities will be then verified and utilized to launch a fully automated, near real-time flare forecasting service. EXPECTED RESULTSFLARECAST will push the envelope of current understanding of solar active-region properties and their relation to flaring activity. In parallel, a functionally-expandable infrastructure will accommodate flare predictions allowing a simple, but suitably verified, transition of scientific research into an operational space-weather application. The resulting user-friendly, interactive facility will be freely accessible to researchers and operators in Europe and around the globe.PROJECT PARTNERS------------------------AA: Academy of Athens GreeceTCD: Trinity College Dublin IrelandUNIGE: Universita Degli Studi Di Genova ItalyCNR: Consiglio Nazionale Delle Ricerche ItalyCNRS: Centre National de la Recherche Scientifique FranceUPSud: Université Paris-Sud FranceFHNW: Fachhochschule Nordwestschweiz SwitzerlandMO: Met Office United KingdomUNN: University of Northumbria at Newcastle United Kingdom Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far SECOND PERIODIC REPORT (JANUARY 2016 – DECEMBER 2017): LIST OF ABBREVIATIONS USED: CME : coronal mass ejectionDB : DatabaseLOS : Line of Sight ML: : Machine Learning SHARP : Space Weather HMI Active Region Patch WP : Work Package THE PROJECT'S THREE OBJECTIVES: 1. To understand the drivers of flare activity and improve flare prediction.2. To provide a globally accessible flare prediction service that facilitates expansion. 3. To engage with space-weather end users and inform policy makers and the public.WORK OVER FIRST OBJECTIVE IN YEARS 2 – 3: Wps 2, 3, 5 and 6:WP2 (solar active region properties as predictors of flare activity) was the first to be finalized, because the prediction DB was a prerequisite for the prediction and verification DBs to be filled. WP2 gave rise to two of its three deliverables, with the latest one (D2.4) referring to the finalized predictor algorithms, metadata and documentation. The project produced a total of 171 predictors for each SHARP analyzed. WP3 (flare prediction algorithms) exploited the prediction DB by implementing a total of 21 ML algorithms on top of 8 non-ML ones. Four out of five deliverables of WP3 was produced in Years 2 – 3 with the last one (D3.5) conveying the final state of the ML prediction algorithms, where the bulk of prediction work is made. WP5 (data and forecast validation) was entirely implemented in Year 2 – 3. Work was structured and revolved around 3 deliverables that pertained to the production of the forecast verification software, associated uncertainties and the data monitoring software. A total of 20 metrics, skill scores and discriminants were used to verify data and forecasts. WP6 (explorative research) was also entirely implemented in Years 2 – 3. Revolving around objectives to understand flares, to investigate the role of forecast window and latency in flare forecasting and to improve the flare – CME connection, it produced 3 deliverables and a number of refereed papers, with more manuscripts in various preparatory stages. WORK OVER SECOND OBJECTIVE IN YEARS 2 – 3: WP4. WP4 (data storage and processing cloud) aimed to prepare the FLARECAST technology element and to create the infrastructure to be used for processing, forecasting, and visualization. It combined the efficient exploitation of big data and archive technology and documented this work in 6 deliverables, five of which were completed in Years 2 – 3. The finalized infrastructure framework and prediction database schema are described in deliverables D4.5 and D4.6 respectively. WORK OVER THIRD OBJECTIVE IN YEARS 2 – 3: WP7WP7 (dissemination) aimed toward communication with the scientific community, users and stakeholders, and the public. While the first and the third aims originated in the first period, the second aim was exclusively completed during the second period: in Year 3 the project organized two User / Stakeholder Workshops in the MO partner and during the 2017 European Space Weather Week, respectively. The Workshops are presented and discussed in deliverables D7.6 and D7.7 respectively. In addition, a survey capturing the understanding and level of knowledge of the user community toward flare prediction was implemented. Of a total of 9 deliverables in WP7, 5 were fullfiled in Years 2 – 3. Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) FLARECAST has managed to push the envelope of current understanding of solar active-region properties and their relation to flaring activity. This goal was decisively advanced in Years 2 – 3 with the accumulation of (1) the largest number of predictor parameters ever used for flare forecasting, (2) the largest number of assembled prediction algorithms , (3) a set of new flare, and even CME, predictors and (4) an interactive, customizable, fully verified flare prediction facility, open to scientists, operators and industry professional in Europe and around the globe.