105 citations · 105 across the 1 of their papers we have counts for
8 papers
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…
Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium
Tristan H. Abbott, Timothy W. Cronin, Tom Beucler
Tropical precipitation extremes are expected to strengthen with warming, but quantitative estimates remain uncertain because of a poor understanding of changes in convective dynami…
Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
Tom Beucler, Michael Pritchard, Stephan Rasp +3
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints. Here, we int…