DeepCloud - A data-driven sub-grid scale parametrization for complex terrain

DeepCloud - A data-driven sub-grid scale parametrization for complex terrain - An SDSC collaborative data science project with ETH Zurich
There is considerable consensus that the main cause of the great uncertainties in future changes in precipitation is the need to parameterize sub-grid scale (SGS) processes in climate models (GCM). The key source of error in the recent generation of GCMs is believed to be the interaction between SGS processes and the large-scale atmospheric circulation that is due to its temporal and spatial scale explicitly resolved. While there is hope on the long run, because the projected increase in computer power will allow us to resolve many SGS processes in the upcoming decade, researchers are currently investigating how the representation of SGS processes can be improved using machine learning (ML), and there are still considerable challenges ahead.
Presented by Prof. Dr. Sebastian Schemm, ETH Zürich
With:
Dr. Guillaume Bertoli, Atmospheric Circulation, ETHZ
Dr. Stefan Rüdisühli, Center for Climate Systems Modeling, ETHZ
Dr. Eniko Szekely
Dr. Firat Ozdemir (SDSC)
More Info Here: www.datascience.ch/projects/d...