Zobrazeno 1 - 10 of 21 pro vyhledávání: '"Oliver Watt‐Meyer"'
1
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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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
Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection
Autor: Clayton Sanford, Anna Kwa, Oliver Watt‐Meyer, Spencer K. Clark, Noah Brenowitz, Jeremy McGibbon, Christopher Bretherton
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 15, Iss 11, Pp n/a-n/a (2023)
Abstract Using machine learning (ML) for the online correction of coarse‐resolution atmospheric models has proven effective in reducing biases in near‐surface temperature and precipitation rate. However, ML corrections often introduce new biases
Externí odkaz: https://doaj.org/article/584587531857495799f951ea1bfd98ee
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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
Improving the reliability of ML-corrected climate models with novelty detection
Autor: Clayton Hendrick Sanford, Anna Kwa, Oliver Watt-Meyer, Spencer Koncius Clark, Noah Domino Brenowitz, Jeremy McGibbon, Christopher S. Bretherton
The use of machine learning (ML) for the online correction of coarse-resolution atmospheric models has proven effective in reducing biases in near-surface temperature and precipitation rate. However, this often introduces biases in the upper atmosphe
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2870f70610b75c3736306a91b1a6da78
https://doi.org/10.22541/essoar.168500343.32924398/v1
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