EGU call for abstracts: ST4.5 Prediction of Solar Flares and Eruptions

December 11, 2018, from robertus erdelyi

Dear Colleagues,

We would like to draw your attention and invite you to consider submitting an abstract to session ST4.5 in the Space Weather and Space Climate programme group to be held at the EGU General Assembly 2019, April 7–12, in Vienna

Abstract submission:

The abstract deadline is 10 January 2019, 13:00 CET.

ST4.5 Prediction of Solar Flares and Eruptions: Observations, Theory and Modeling
Session details:
The session is intended as a discussion forum for reviewing and improving our current understanding of solar flare occurrence mechanisms and the prediction of flares and eruptions in both observational and modeling settings. In particular, this session will discuss, first, the apparent paradigm shift from simple flare and eruption prediction methods to interdisciplinary, multi-parameter investigations enabled by artificial intelligence (AI) and, second, the current and future synergies between academic and operational sectors in the framework of research to operations (R2O). Solar eruptions cause space weather phenomena that can affect space environment and sometimes impact our infrastructure, causing disruptions to our societal fabric. Prediction of solar flares and eruptions is essential to increase the lead time and the accuracy of space weather forecasts. Synergies are crucial for establishing operational prediction models and for effectively evaluating and validating these models. Such collaborative approaches are motivated by observational advances enabled by space missions (SDO, STEREO, SOHO, Hinode, RHESSI, GOES, Parker Solar Probe, and Solar Orbiter in the near future, etc.), empirical human forecasting for decades, statistical methods, advances in machine- and deep-learning techniques, big-data handling, as well as realistic, data-driven numerical simulations. We solicit contributions on solar flare and eruption prediction, including operational human forecasting, statistical models, AI investigations and state-of-the-art forecast models enabled by numerical simulations, aiming toward future operations. Abstracts on data and performance verification, validation and benchmarking are also welcome.

We look forward to receiving your contributions and thank you very much for your attention.

Sincerely yours, session conveners,
Mamoru Ishii,
Manolis Georgoulis ,
KD Leka,
Naoto Nishizuka