no code implementations • 30 Oct 2023 • Hugo Frezat, Ronan Fablet, Guillaume Balarac, Julien Le Sommer
It is demonstrated that training the neural emulator and parametrization components separately with different loss quantities is necessary in order to minimize the propagation of approximation biases.
no code implementations • 19 Sep 2023 • Quentin Febvre, Julien Le Sommer, Clément Ubelmann, Ronan Fablet
Here, we leverage both simulations of ocean dynamics and satellite altimeters to train simulation-based neural mapping schemes for the sea surface height and demonstrate their performance for real altimetry datasets.
no code implementations • 9 Feb 2023 • Quentin Febvre, Clément Ubelmann, Julien Le Sommer, Ronan Fablet
Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics.
no code implementations • 23 Nov 2022 • Ronan Fablet, Bertrand Chapron, Julien Le Sommer, Florian Sévellec
This is however limited to the surface-constrained geostrophic component of sea surface velocities.
1 code implementation • 18 Nov 2022 • J. Emmanuel Johnson, Redouane Lguensat, Ronan Fablet, Emmanuel Cosme, Julien Le Sommer
Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences.
1 code implementation • 12 Apr 2022 • Joseph Jenkins, Adeline Paiement, Yann Ourmières, Julien Le Sommer, Jacques Verron, Clément Ubelmann, Hervé Glotin
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data.
no code implementations • 8 Apr 2022 • Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models.
1 code implementation • 12 Nov 2021 • Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
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.
no code implementations • 7 Oct 2021 • Quentin Febvre, Ronan Fablet, Julien Le Sommer, Clément Ubelmann
The proposed framework significantly outperforms the operational state-of-the-art mapping pipeline and truly benefits from wide-swath data to resolve finer scales on the global map as well as in the SWOT sensor geometry.
1 code implementation • 9 Oct 2020 • Hugo Frezat, Guillaume Balarac, Julien Le Sommer, Ronan Fablet, Redouane Lguensat
In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs).
1 code implementation • 3 May 2020 • Redouane Lguensat, Ronan Fablet, Julien Le Sommer, Sammy Metref, Emmanuel Cosme, Kaouther Ouenniche, Lucas. Drumetz, Jonathan Gula
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus allowing for a better characterization of the mesoscale and submesoscale eddy field.
1 code implementation • 20 Nov 2019 • Redouane Lguensat, Julien Le Sommer, Sammy Metref, Emmanuel Cosme, Ronan Fablet
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems.