39 citations · 39 across the 1 of their papers we have counts for
3 papers
Machine Learning Climate Model Dynamics: Offline versus Online Performance
Noah D. Brenowitz, Brian Henn, Jeremy McGibbon +5
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly r…
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…
Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse-graining
Noah D Brenowitz, Christopher S Bretherton
General circulation models (GCMs) typically have a grid size of 25--200 km. Parametrizations are used to represent diabatic processes such as radiative transfer and cloud microphys…