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.

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

Thursday, 11 April 2019 | Room 0.60

Chairperson: Peter Dueben
16:15–16:30 |
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 |
Laure Zanna and Thomas Bolton
16:45–17:00 |
Said Ouala, Ronan Fablet, Van-Duong Nguyen, Lucas Drumetz, Bertrand Chapron, Ananda Pascual, Fabrice Collard, and Lucile Gaultier
17:00–17:15 |
| presentation
Sebastian Scher and Gabriele Messori
17:15–17:30 |
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
17:30–17:45 |
David Gagne, Hannah Christensen, Aneesh Subramanian, and Adam Monahan
17:45–18:00 |
Christoph Keller and Mat Evans