408 citations · 481 across the 6 of their papers we have counts for
Showing 2020Show all
2 papers · 1 filter
physics.ao-ph2020★ 46 cited
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
Griffin Mooers, Mike Pritchard, Tom Beucler +4
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Par…
physics.ao-ph2020
Generative Modeling for Atmospheric Convection
Griffin Mooers, Jens Tuyls, Stephan Mandt +2
While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of comput…