Orals

AS1.6

From the perspective of Earth System predictions, the use of machine learning, and in particular deep learning, is still in its infancy. There are many possible ways how machine learning could improve model quality, generate significant speed-ups for simulations or help to extract information from numerous Earth System data, in particular satellite observations. However, it has yet to be shown that machine learning can hold what it is promising for the specific needs of the application of Earth System predictions. This session aims to provide an overview how machine learning can/will be used in the future and tries to summarise the state-of-the-art in an area of research that is developing at a breathtaking pace.

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Co-organized as CL5.07/ESSI1.5/OS4.25
Convener: Peter Düben | Co-conveners: Julien Brajard, Peter Bauer, Tim Palmer
Orals
| Thu, 11 Apr, 16:15–18:00
 
Room 0.60
Posters
| Attendance Thu, 11 Apr, 14:00–15:45
 
Hall X5

Thursday, 11 April 2019 | Room 0.60

Chairperson: Peter Dueben
16:15–16:30 |
EGU2019-13625
Martin Schultz, Felix Kleinert, Lukas Leufen, Jessica Ahring, Susanne Theis, Jan Keller, Gordon Pipa, Johannes Leugering, Pascal Nieters, Peter Baumann, Vlad Merticariu, Andreas Hense, and Rita Glowienka-Hense
16:30–16:45 |
EGU2019-2947
Laure Zanna and Thomas Bolton
16:45–17:00 |
EGU2019-15363
Said Ouala, Ronan Fablet, Van-Duong Nguyen, Lucas Drumetz, Bertrand Chapron, Ananda Pascual, Fabrice Collard, and Lucile Gaultier
17:00–17:15 |
EGU2019-8028
Sebastian Scher and Gabriele Messori
17:15–17:30 |
EGU2019-6549
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
17:30–17:45 |
EGU2019-11211
David Gagne, Hannah Christensen, Aneesh Subramanian, and Adam Monahan
17:45–18:00 |
EGU2019-11503
Christoph Keller and Mat Evans