Chairs: Nikki Vercauteren, Stephan Pfahl, Annette Müller, Tom Dörffel, Lisa Schielicke

Atmospheric models are classically based on the equations of motion, for which the unresolved scales need to be parameterised. In a complementary approach, statistical and machine learning methods are used to leverage observations and high-resolution model data by providing data-driven models. This mini symposium aims at the application of machine learning approaches for a better understanding of atmospheric dynamics across all scales. Different approaches, techniques and challenges applying machine learning to atmospheric phenomena across all scales will be discussed.

Find all abstracts for all talks here or linked below: