Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Jeremy McGibbon"'
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
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
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
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
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
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
https://doi.org/10.22541/essoar.168500343.32924398/v1
Autor:
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, Oliver Fuhrer
Publikováno v:
Geoscientific Model Development, 16 (9)
Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's law drivin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac70d55edfc0fbd198a3bb34dacb2d8c
Autor:
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, Oliver Fuhrer
Publikováno v:
eISSN
Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's Law drivin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5f7fb7a6b298ccb511a0598216f8fc2
https://doi.org/10.5194/egusphere-2022-943
https://doi.org/10.5194/egusphere-2022-943
Autor:
Anna Kwa, Spencer Koncius Clark, Brian Henn, Noah D Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, W. Andre Perkins, Lucas Harris, Christopher S. Bretherton
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ef7d5014ecbcac9da83ab3fd8b97dfe
https://doi.org/10.1002/essoar.10512393.1
https://doi.org/10.1002/essoar.10512393.1