Zobrazeno 1 - 10 of 14 pro vyhledávání: '"W. Andre Perkins"'
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
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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3
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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4
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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5
Akademický článek
Linear Inverse Modeling for Coupled Atmosphere‐Ocean Ensemble Climate Prediction
Autor: W. Andre Perkins, Greg Hakim
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 12, Iss 1, Pp n/a-n/a (2020)
Abstract Paleoclimate data assimilation (PDA) experiments reconstruct climate fields by objectively blending information from climate models and proxy observations. Due to high computational cost and relatively low forecast skill, most reconstruction
Externí odkaz: https://doaj.org/article/98d604ee8663419bb68941c7181559e2
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10
Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations
Autor: W. Andre Perkins, Oliver Watt-Meyer, Spencer K. Clark, Jeremy McGibbon, Christopher S. Bretherton, Brian Henn, Anna Kwa, Noah D. Brenowitz
Publikováno v: Geophysical Research Letters. 48
Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. These biases are problematic for for...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d864f31eb78c0dc3f6e3f6a5fcc04481
https://doi.org/10.1029/2021gl092555
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