Web: http://arxiv.org/abs/2112.11317

May 11, 2022, 1:12 a.m. | Arthur Grundner, Tom Beucler, Pierre Gentine, Fernando Iglesias-Suarez, Marco A. Giorgetta, Veronika Eyring

cs.LG updates on arXiv.org arxiv.org

A promising approach to improve cloud parameterizations within climate models
and thus climate projections is to use deep learning in combination with
training data from storm-resolving model (SRM) simulations. The ICOsahedral
Non-hydrostatic (ICON) modeling framework permits simulations ranging from
numerical weather prediction to climate projections, making it an ideal target
to develop neural network (NN) based parameterizations for sub-grid scale
processes. Within the ICON framework, we train NN based cloud cover
parameterizations with coarse-grained data based on realistic regional and …

arxiv cloud deep deep learning learning physics

More from arxiv.org / cs.LG updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California