Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Pavel Perezhogin"'
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 10, Pp n/a-n/a (2024)
Abstract Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies
Externí odkaz:
https://doaj.org/article/96a008bac6ae41ae9e751e297cbd8e90
Autor:
Cheng Zhang, Pavel Perezhogin, Cem Gultekin, Alistair Adcroft, Carlos Fernandez‐Granda, Laure Zanna
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 10, Pp n/a-n/a (2023)
Abstract We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zann
Externí odkaz:
https://doaj.org/article/468790954e424dcba55f56671134727f
Autor:
Pavel Perezhogin, Andrey Glazunov
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 10, Pp n/a-n/a (2023)
Abstract Ocean models at intermediate resolution (1/4°), which partially resolve mesoscale eddies, can be seen as Large eddy simulations of the primitive equations, in which the effect of unresolved eddies must be parameterized. In this work, we pro
Externí odkaz:
https://doaj.org/article/97e76d72b63a41f5b8650d672417dde8
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 10, Pp n/a-n/a (2023)
Abstract Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data‐driven methods, with uncertainty quantification. For examp
Externí odkaz:
https://doaj.org/article/ab24e754d5fd4e2c9bd283afe4d7f57a
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 1, Pp n/a-n/a (2023)
Abstract Recently, a growing number of studies have used machine learning (ML) models to parameterize computationally intensive subgrid‐scale processes in ocean models. Such studies typically train ML models with filtered and coarse‐grained high
Externí odkaz:
https://doaj.org/article/2bb24ce9edc845958b07fa3c8a434f51
