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of 52
pro vyhledávání: '"Perezhogin, Pavel A."'
Autor:
Gultekin, Cem, Subel, Adam, Zhang, Cheng, Leibovich, Matan, Perezhogin, Pavel, Adcroft, Alistair, Fernandez-Granda, Carlos, Zanna, Laure
Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the climate system. Parameterization is an approach to capture the effect
Externí odkaz:
http://arxiv.org/abs/2411.06604
This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023). We focus on the boundary con
Externí odkaz:
http://arxiv.org/abs/2411.01138
Publikováno v:
Journal of Advances in Modeling Earth Systems, 16, e2023MS004104
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, can be
Externí odkaz:
http://arxiv.org/abs/2311.02517
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution simulations. However, when trained in a purely offline mode, integratin
Externí odkaz:
http://arxiv.org/abs/2307.13144
Publikováno v:
Physical Review E 109, 044202 (2024)
We propose a data-driven framework to simplify the description of spatiotemporal climate variability into few entities and their causal linkages. Given a high-dimensional climate field, the methodology first reduces its dimensionality into a set of r
Externí odkaz:
http://arxiv.org/abs/2306.14433
Autor:
Perezhogin, Pavel, Glazunov, Andrey
Ocean models at intermediate resolution (1/4 degree), which partially resolve mesoscale eddies, can be seen as Large eddy simulations (LES) of the primitive equations, in which the effect of unresolved eddies must be parameterized. In this work, we p
Externí odkaz:
http://arxiv.org/abs/2304.06789
Autor:
Zhang, Cheng, Perezhogin, Pavel, Gultekin, Cem, Adcroft, Alistair, Fernandez-Granda, Carlos, Zanna, Laure
We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one particular machine learned parameterization \cite{Guillaumin1&Zanna-J
Externí odkaz:
http://arxiv.org/abs/2303.00962
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 example, Guillau
Externí odkaz:
http://arxiv.org/abs/2302.07984
Autor:
Perezhogin, Pavel1 (AUTHOR) pp2681@nyu.edu, Zhang, Cheng2 (AUTHOR), Adcroft, Alistair2 (AUTHOR), Fernandez‐Granda, Carlos1,3 (AUTHOR), Zanna, Laure1 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Oct2024, Vol. 16 Issue 10, p1-27. 27p.
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