Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Sommer, Julien Le"'
For reasons of computational constraint, most global ocean circulation models used for Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations in these parameterizations affect the modeled ocean circulation and im
Externí odkaz:
http://arxiv.org/abs/2411.14106
In this paper, we propose a generic algorithm to train machine learning-based subgrid parametrizations online, i.e., with a posteriori loss functions, but for non-differentiable numerical solvers. The proposed approach leverages a neural emulator to
Externí odkaz:
http://arxiv.org/abs/2310.19385
Autor:
Johnson, J. Emmanuel, Febvre, Quentin, Gorbunova, Anastasia, Metref, Sammy, Ballarotta, Maxime, Sommer, Julien Le, Fablet, Ronan
The ocean profoundly influences human activities and plays a critical role in climate regulation. Our understanding has improved over the last decades with the advent of satellite remote sensing data, allowing us to capture essential quantities over
Externí odkaz:
http://arxiv.org/abs/2309.15599
Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL) schemes have emerged as appealing solutions to address space-time inte
Externí odkaz:
http://arxiv.org/abs/2309.14350
Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which prov
Externí odkaz:
http://arxiv.org/abs/2302.04497
Satellite altimetry is a unique way for direct observations of sea surface dynamics. This is however limited to the surface-constrained geostrophic component of sea surface velocities. Ageostrophic dynamics are however expected to be significant for
Externí odkaz:
http://arxiv.org/abs/2211.13059
Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences. With the influx of more satellite missions, we have access to more and more observations and it is becoming more perti
Externí odkaz:
http://arxiv.org/abs/2211.10444
Autor:
Jenkins, Joseph, Paiement, Adeline, Ourmières, Yann, Sommer, Julien Le, Verron, Jacques, Ubelmann, Clément, Glotin, Hervé
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach require
Externí odkaz:
http://arxiv.org/abs/2204.05891
Publikováno v:
Journal of Advances in Modeling Earth Systems. Volume 14, Issue 11 (November 2022)
The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based o
Externí odkaz:
http://arxiv.org/abs/2204.03911
Modeling the subgrid-scale dynamics of reduced models is a long standing open problem that finds application in ocean, atmosphere and climate predictions where direct numerical simulation (DNS) is impossible. While neural networks (NNs) have already
Externí odkaz:
http://arxiv.org/abs/2111.06841