Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Lguensat, Redouane"'
Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well un
Externí odkaz:
http://arxiv.org/abs/2310.13916
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
The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi-scale dynamics. By considering a toy climate model, namely, the two-scale Lorenz96 model and producing experiments in perfect-model
Externí odkaz:
http://arxiv.org/abs/2208.06243
Autor:
Clare, Mariana C. A., Sonnewald, Maike, Lguensat, Redouane, Deshayes, Julie, Balaji, Venkatramani
The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision-making such as in cli
Externí odkaz:
http://arxiv.org/abs/2205.00202
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
Autor:
Sonnewald, Maike, Lguensat, Redouane, Jones, Daniel C., Dueben, Peter D., Brajard, Julien, Balaji, Venkatramani
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of
Externí odkaz:
http://arxiv.org/abs/2104.12506
Autor:
Harder, Paula, Jones, William, Lguensat, Redouane, Bouabid, Shahine, Fulton, James, Quesada-Chacón, Dánell, Marcolongo, Aris, Stefanović, Sofija, Rao, Yuhan, Manshausen, Peter, Watson-Parris, Duncan
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night. The gap can be filled by employing available infra-red observations to generate visi
Externí odkaz:
http://arxiv.org/abs/2011.07017
Publikováno v:
Phys. Rev. Fluids 6, 024607 (2021)
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). When trained from direct numerical simulation (DNS) data, state-of-the-a
Externí odkaz:
http://arxiv.org/abs/2010.04663
Autor:
Lguensat, Redouane, Fablet, Ronan, Sommer, Julien Le, Metref, Sammy, Cosme, Emmanuel, Ouenniche, Kaouther, Drumetz, Lucas, Gula, Jonathan
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 submesoscal
Externí odkaz:
http://arxiv.org/abs/2005.01090