459 citations · 966 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
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…
Interpreting and Stabilizing Machine-learning Parametrizations of Convection
Noah D. Brenowitz, Tom Beucler, Michael Pritchard +1
Neural networks are a promising technique for parameterizing sub-grid-scale physics (e.g. moist atmospheric convection) in coarse-resolution climate models, but their lack of inter…
Quantifying Convective Aggregation using the Tropical Moist Margin's Length
Tom Beucler, David Leutwyler, Julia Windmiller
On small scales, the tropical atmosphere tends to be either moist or very dry. This defines two states that, on large scales, are separated by a sharp margin, well-identified by th…
Towards Physically-consistent, Data-driven Models of Convection
Tom Beucler, Michael Pritchard, Pierre Gentine +1
Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate…