Unsupervised, supervised, semi-supervised as well as reinforcement learning are now increasingly used to address Earth system related challenges.
Machine learning could help extract information from numerous Earth System data, such as in-situ and satellite observations, as well as improve model fidelity through novel parameterisations or speed-ups. This session invites submissions spanning modelling and observational approaches towards providing an overview of the state-of-the-art of the application of these novel methods. This includes (but it is not restricted to):
- the use of machine learning to improve forecast skill
- generate significant speedups
- design new parameterization schemes
- emulate numerical models.
Please consider submitting abstracts focussed on ML applied to observations and modelling of climate processes to the companion "ML for Climate Science" session.