Posters

ST4.5

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.
Moreover, the long-term effect of solar spectral irradiance (SSI) of the UV and longer wavelength on the Earth climate system have been studied in detail, however the effect of particle precipitation on the Earth's atmosphere and climate has only recently received increasing attention. To cover these aspects, abstracts are welcome that discuss the impact of space weather events on the terrestrial atmosphere for different time scales.

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Convener: Ishii Mamoru | Co-conveners: Manolis Georgoulis, Margit Haberreiter, Leka KD, Naoto Nishizuka
Orals
| Tue, 09 Apr, 08:30–10:15
 
Room 2.44
Posters
| Attendance Wed, 10 Apr, 10:45–12:30
 
Hall X4

Attendance time: Wednesday, 10 April 2019, 10:45–12:30 | Hall X4

X4.170 |
EGU2019-2952
Rajesh Vaishnav, Christoph Jacobi, Jens Berdermann, Mihail Codrescu, and Erik Schmölter
X4.171 |
EGU2019-10726
Timo Asikainen, Antti Salminen, Ville Maliniemi, and Kalevi Mursula
X4.172 |
EGU2019-17300
Margit Haberreiter
X4.174 |
EGU2019-17564
Francesco Berrilli, Alberto Bigazzi, Carlo Cauli, Dario Del Moro, Luca Giovannelli, and Mija Lovric
X4.175 |
EGU2019-18294
Suzy Bingham, David Jackson, Michael Sharpe, Sophie Murray, Jesse Andries, and Catherine Burnett
X4.176 |
EGU2019-18149
András Ludmány, Tünde Baranyi, and Judit Muraközy
X4.177 |
EGU2019-8093
Utilizing remote sensing and GPS satellites combined with ground-based observations for producing real-time space weather predictions using machine learning techniques
(withdrawn)
Eric Rosenberg, Saed Asaly, Yuval Reuveni, and Lee-Ad Gottlieb
X4.178 |
EGU2019-17717
Mamoru Ishii, Daikou shiota, and Chihiro Tao
X4.180 |
EGU2019-17832
Luca Giovannelli, Francesco Berrilli, Domenico Cicogna, and Dario Del Moro
X4.181 |
EGU2019-15364
Emma Bland, Noora Partamies, and Erkka Heino
X4.182 |
EGU2019-12419
| Highlight
Naoto Nishizuka, Komei Sugiura, Yuki Kubo, Mitsue Den, and Mamoru Ishii
X4.183 |
EGU2019-3118
Machine learning for flare forecasting within the FLARECAST platform
(withdrawn after no-show)
Anna Maria Massone, Michele Piana, Cristina Campi, and Federico Benvenuto
X4.184 |
EGU2019-5329
Erik Schmölter, Jens Berdermann, Norbert Jakowski, Christoph Jacobi, and Rajesh Vaishnav