Zobrazeno 1 - 10 of 52 pro vyhledávání: '"Noah D. Brenowitz"'
1
Akademický článek
Emulation of Cloud Microphysics in a Climate Model
Autor: W. Andre Perkins, Noah D. Brenowitz, Christopher S. Bretherton, Jacqueline M. Nugent
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 16, Iss 4, Pp n/a-n/a (2024)
Abstract We present a machine learning based emulator of a microphysics scheme for condensation and precipitation processes (Zhao‐Carr) used operationally in a global atmospheric forecast model (FV3GFS). Our tailored emulator architecture achieves
Externí odkaz: https://doaj.org/article/fe0631a5116b42efbfbab7c1a6905f75
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2
Akademický článek
A Machine Learning Parameterization of Clouds in a Coarse‐Resolution Climate Model for Unbiased Radiation
Autor: Brian Henn, Yakelyn R. Jauregui, Spencer K. Clark, Noah D. Brenowitz, Jeremy McGibbon, Oliver Watt‐Meyer, Andrew G. Pauling, Christopher S. Bretherton
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 16, Iss 3, Pp n/a-n/a (2024)
Abstract Coarse‐grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target for a machine‐learned parameterization. We machine
Externí odkaz: https://doaj.org/article/ad161f42df774de1aab4a8332692ca71
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3
Akademický článek
Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation
Autor: Oliver Watt‐Meyer, Noah D. Brenowitz, Spencer K. Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, Lucas Harris, Christopher S. Bretherton
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 16, Iss 2, Pp n/a-n/a (2024)
Abstract Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving
Externí odkaz: https://doaj.org/article/e8eeeac9be0a41338c896fabcc34c313
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4
Akademický článek
Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle
Autor: Anna Kwa, Spencer K. Clark, Brian Henn, Noah D. Brenowitz, Jeremy McGibbon, Oliver Watt‐Meyer, W. Andre Perkins, Lucas Harris, Christopher S. Bretherton
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 15, Iss 5, Pp n/a-n/a (2023)
Abstract One approach to improving the accuracy of a coarse‐grid global climate model is to add machine‐learned (ML) state‐dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine
Externí odkaz: https://doaj.org/article/09772585dee748f782ed267f024303ba
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5
Akademický článek
Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations
Autor: Christopher S. Bretherton, Brian Henn, Anna Kwa, Noah D. Brenowitz, Oliver Watt‐Meyer, Jeremy McGibbon, W. Andre Perkins, Spencer K. Clark, Lucas Harris
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 14, Iss 2, Pp n/a-n/a (2022)
Abstract Global atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally a
Externí odkaz: https://doaj.org/article/29092340145144bfbb4ddce30ea9c04c
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6
Akademický článek
Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining
Autor: Noah D. Brenowitz, Christopher S. Bretherton
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 11, Iss 8, Pp 2728-2744 (2019)
Abstract 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 microphysics and account for subgrid‐scale motions and variability.
Externí odkaz: https://doaj.org/article/2f8e5c4b48614e63a9ba7f952ee67833
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