Zobrazeno 1 - 10
of 915
pro vyhledávání: '"MCWILLIAMS, JAMES C."'
The spontaneous emission of internal waves (IWs) from balanced mesoscale eddies has been previously proposed to provide a source of oceanic IW kinetic energy (KE). This study examines the mechanisms leading to the spontaneous emission of spiral-shape
Externí odkaz:
http://arxiv.org/abs/2409.10758
This work proposes a general framework for capturing noise-driven transitions in spatially extended non-equilibrium systems and explains the emergence of coherent patterns beyond the instability onset. The framework relies on stochastic parameterizat
Externí odkaz:
http://arxiv.org/abs/2408.13428
This study investigates the influence of suspended kelp farms on ocean mixed layer hydrodynamics in the presence of currents and waves. We use the large eddy simulation method, where the wave effect is incorporated by solving the wave-averaged equati
Externí odkaz:
http://arxiv.org/abs/2403.04947
Autor:
Capó, Esther, McWilliams, James C., Gula, Jonathan, Molemaker, M. Jeroen, Damien, Pierre, Schubert, René
Realistic computational simulations in different oceanic basins reveal prevalent prograde mean flows (i.e. in the direction of topographic Rossby wave propagation along isobaths; a.k.a. topostrophy) on topographic slopes in the deep ocean, consistent
Externí odkaz:
http://arxiv.org/abs/2402.11152
A general, variational approach to derive low-order reduced systems is presented. The approach is based on the concept of optimal parameterizing manifold (OPM) that substitutes the more classical notions of invariant or slow manifold when breakdown o
Externí odkaz:
http://arxiv.org/abs/2307.06537
Recent years have seen a surge in interest for leveraging neural networks to parameterize small-scale or fast processes in climate and turbulence models. In this short paper, we point out two fundamental issues in this endeavor. The first concerns th
Externí odkaz:
http://arxiv.org/abs/2305.04331
Turbulence closure with small, local neural networks: Forced two-dimensional and $\beta$-plane flows
We parameterize sub-grid scale (SGS) fluxes in sinusoidally forced two-dimensional turbulence on the $\beta$-plane at high Reynolds numbers (Re$\sim$25000) using simple 2-layer Convolutional Neural Networks (CNN) having only O(1000)parameters, two or
Externí odkaz:
http://arxiv.org/abs/2304.05029
Publikováno v:
In Progress in Oceanography December 2024 229
A central challenge in physics is to describe non-equilibrium systems driven by randomness, such as a randomly growing interface, or fluids subject to random fluctuations that account e.g. for local stresses and heat fluxes not related to the velocit
Externí odkaz:
http://arxiv.org/abs/2202.07031