Publications

Correcting a 200 km resolution climate model in multiple climates by machine learning from 25 km resolution simulations
Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to …
Spencer K Clark, Noah D Brenowitz, Brian Henn, Anna Kwa, Jeremy McGibbon, W Andre Perkins, Oliver Watt-Meyer, Christopher S Bretherton, Lucas M Harris
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Correcting coarse‐grid weather and climate models by machine learning from global storm‐resolving simulations
Global atmospheric ?storm-resolving? models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in …
Christopher S Bretherton, Brian Henn, Anna Kwa, Noah D Brenowitz, Oliver Watt-Meyer, Jeremy McGibbon, W Andre Perkins, Spencer K Clark, Lucas Harris
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Correcting weather and climate models by machine learning nudged historical simulations
Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to …
Oliver Watt-Meyer, Noah D Brenowitz, Spencer K Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W Andre Perkins, Christopher S Bretherton
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Correcting weather and climate models by machine learning nudged historical simulations
Machine Learning Climate Model Dynamics: Offline versus Online Performance
Noah D Brenowitz, Brian Henn, Spencer Clark, Anna Kwa, Jeremy McGibbon, W Andre Perkins, Oliver Watt-Meyer, Christopher S Bretherton
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Effects of Rotation on the Multiscale Organization of Convection in a Global 2D Cloud-Resolving Model
Atmospheric convection exhibits distinct spatiotemporal variability at different latitudes. A good understanding of the effects of …
Qiu Yang, Andrew J. Majda, Noah D. Brenowitz
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Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse-graining
General circulation models (GCMs) typically have a grid size of 25–200 km. Parametrizations are used to represent diabatic …
Noah D Brenowitz, Christopher S. Bretherton
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Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse-graining
The Multiscale Impacts of Organized Convection in Global 2D cloud-resolving Models
This paper studies the mechanisms behind the multiscale organization of tropical moist convection using a trio of cloud‐resolving …
Noah D. Brenowitz, Andrew J. Majda, Qiu Yang
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The Multiscale Impacts of Organized Convection in Global 2D cloud-resolving Models
Prognostic validation of a neural network unified physics parameterization
Weather and climate models approximate diabatic and sub-grid-scale processes in terms of grid-scale variables using parameterizations. …
Noah D. Brenowitz, Cristopher S. Bretherton
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Prognostic validation of a neural network unified physics parameterization
Nonlinear Laplacian spectral analysis of Rayleigh–Benard convection
The analysis of physical datasets using modern methods developed in machine learning presents unique challenges and opportunities. These datasets typically feature many degrees of freedom, which tends to increase the computational cost of statistical methods and complicate interpretation.
Noah D. Brenowitz, Dimitris Giannakis, Andrew J.Majda
Non-local convergence coupling in a simple stochastic convection model
Observational studies show a strong correlation between large-scale wind convergence and precipitation. However, using this as a …
Noah D. Brenowitz, Yevgeniy Frenkel, Andrew J.Majda
Enhanced persistence of equatorial waves via convergence coupling in the stochastic multicloud model
Recent observational and theoretical studies show a systematic relationship between tropical moist convection and measures related to …
Noah D. Brenowitz, Yevgeniy Frenkel, Andrew J.Majda