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Flare Likelihood and Region Eruption Forecasting

Periodic Reporting for period 1 - FLARECAST (Flare Likelihood and Region Eruption Forecasting)

Reporting period: 2015-01-01 to 2015-12-31

TITLE: FLARECAST – Flare Likelihood and Region Eruption Forecasting.
FUNDING AGENCY: EC Horizon 2020, Research and Innovation Action, Protec-1-2014: Space Weather.
GRANT AMOUNT: EUR 2,400 000.00.
DURATION: 3 years.

START DATE: 1 January 2015.
CONSORTIUM: 8 institutions from 6 countries.
PROJECT COORDINATOR: Dr. Manolis K. Georgoulis, Academy of Athens, Greece.
PROJECT SCIENTIST: Dr. D. Shaun Bloomfield, Trinity College Dublin, Ireland.

KEY WORDS: Flare Forecasting, Space Weather, Heliophysics, Prediction Algorithms, Machine Learning.

Space 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. The main agents of space weather being solar flares and coronal mass ejections, FLARECAST will significantly advance our ability to predict flares prior to their occurrence in the Sun.

FLARECAST 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.

Diverse properties of solar active regions, the ""hotspots"" of large flares, will be treated as flare predictors. These properties will be extracted using advanced image-processing techniques applied to remote-sensing solar observations. Various flare prediction algorithms, some of them featuring artificial intelligence, will highlight a statistically rigorous set of the most promising of these predictors. Flare-forecast probabilities will be benchmarked by means of state-of-the-art validation techniques and will be utilized to launch a fully automated, near real-time flare forecasting service, where the end user will be allowed to perform his/her own tests and produce validated results.

FLARECAST 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, the first of its kind in the world, will be freely accessible to researchers and operators in Europe and around the globe.

AA: Academy of Athens Greece.
TCD: Trinity College Dublin Ireland.
UNIGE: Universita Degli Studi Di Genova Italy.
CNR: Consiglio Nazionale Delle Ricerche Italy.
CNRS: Centre National de la Recherche Scientifique France.
UPSud: Université Paris-Sud France.
FHNW: Fachhochschule Nordwestschweiz Switzerland.
MO: Met Office United Kingdom.


DB : Database.
EPO : Education and Public Outreach.
GOES : Geostationary Operational Environmental Satellites.
HARP : HMI Active Region Patch.
HESPE : High Energy Solar Physics Data in Europe.
HMI : Helioseismic and Magnetic Imager.
LOS : Line of Sight.
NOAA : National Oceanic and Atmospheric Administration.
SDO : Solar Dynamics Observatory.
SHARP : Space Weather HMI Active Region Patch.
SMART : SolarMonitor Active Region Tracking.
SWPC : Space Weather Prediction Center.
WP : Work Package.


1. To understand the drivers of flare activity and improve flare prediction. FLARECAST aims to consolidate and advance our understanding of solar active region properties (sunspot structure classifications, magnetic polarity-mixing classifications and physical quantities derived from line-of-sight and vector magnetograms) and their evolution that lead to the production of solar flares. Both state-of-the-art and newly developed prediction algorithms will exploit this enhanced knowledge to identify the best combinations of flare predictors and techniques to be used by different space-weather end users.

2. To provide a globally accessible flare prediction service that facilitates expansion. FLARECAST aims to implement the diverse range of techniques that exist for predicting flares and develop novel techniques, thereby enhancing our capacity to deliver flare forecasts. To do this, FLARECAST mobilizes among the best expertise in Europe on flare prediction, solar data processing and archiving, web-service and interface development, forecast verification and 24/7 operational space-weather forecasting. The final system will feature an algorithm “plug-in” infrastructure framework feeding a data processing cloud that will allow users in research and industry to expand and exploit the available prediction services.

3. To engage with space-weather end users and inform policy makers and the public. FLARECAST aims to deliver the first operationally verified, near-realtime predictions of solar flares to space-weather forecasters, the scientific and industrial sectors and the general public. In parallel with delivering forecasts, FLARECAST engages with the general public, science educators and policy makers to promote the relevance of space weather and its impacts on our day-to-day lives.


The first objective involves WPs 2, 3, 5, and 6.

WP2 (solar active region properties as predictors of flare activity) was fully deployed in Year 1: about 25 different active-region properties, besides properties provided automatically by the SMART utility and besides the NOAA / SWPC- and GOES-provided information are currently under development, reproduction or testing (Table 1). These represent a vast part of the predictors that have been proposed in the literature over the past twenty years or so. A few are still to be fully tested, with this task continuing into Year 2. In addition, consortium members have contacted all developers of original flare predictors to determine whether they are willing to make their codes available for the project. Most of them have already responded positively, which leaves only a small set of proposed predictors to be reproduced by the project from scratch. WP2 has provided one (1) Deliverable report in Year 1, with the second one due in the beginning of Year 2 (after Month 12). Besides consortium staff members, three (3) postdoctoral researchers (two [2] in TCD; one [1] in AA partners) were employed and worked on WP2 in Year 1. The first WP2 milestone (completion of the property database) will be achieved in Year 2. 

WP3 (flare prediction algorithms) has seen consistent work in Year 1 in the areas of data handling and standard machine-learning algorith
FLARECAST will strive to push the envelope of current understanding of solar active-region properties and their relation to flaring activity. This goal will be achieved by the most systematic effort ever undertaken to assemble all proposed flare predictors worldwide, evaluate them robustly on common, established criteria with respect to their predictive ability and determine which parameters, or combinations thereof, are the most competent predictors. In parallel, a functionally-expandable infrastructure will accommodate predictions, thus featuring a simple, but suitably verified, transition of scientific research into an operational space-weather application.

The project is designed to help protect humanity's assets in space, in particular these assets that we have grown increasingly dependent on in recent years.

The resulting interactive, customizable, flare prediction facility will be the first of its kind in the world. The facility will be freely accessible to all end users, from researchers to operators to industry and government professionals, in Europe and around the globe.
Figure 2: The FLARECAST banner for Education and Public Outreach Purposes
Figure 1: Architecture of the FLARECAST computing infrastructure
Table 1: Current database of active region properties to be used as flare predictors