Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Chekroun, Mickael"'
We introduce a generalization of linear response theory for mixed jump-diffusion models, combining both Gaussian and L\'evy noise forcings that interact with the nonlinear dynamics. This class of models covers a broad range of stochastic chaos and co
Externí odkaz:
http://arxiv.org/abs/2411.14769
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
Systems far from equilibrium approach stability slowly due to "anti-mixing" characterized by regions of the phase-space that remain disconnected after prolonged action of the flow. We introduce the Optimal Growth Mode (OGM) to capture this slow initi
Externí odkaz:
http://arxiv.org/abs/2407.02545
Cloud microphysics studies include how tiny cloud droplets grow, and become rain. This is crucial for understanding cloud properties like size, lifespan, and impact on climate through radiative effects. Small, weak-updraft clouds near the haze-to-clo
Externí odkaz:
http://arxiv.org/abs/2405.16556
Ubiquitous, yet elusive to a complete understanding: Tiny, warm clouds with faint visual signatures play a critical role in Earth's energy balance. These "twilight clouds", as they are sometimes called, form under weak updraft conditions. Their const
Externí odkaz:
http://arxiv.org/abs/2405.11545
Autor:
Chekroun, Mickaël D., Liu, Honghu
Conceptual delay models have played a key role in the understanding of El Ni\~no-Southern Oscillation (ENSO) variability. Based on such delay models, we propose a novel scenario for the fabric of ENSO variability resulting from the subtle interplay b
Externí odkaz:
http://arxiv.org/abs/2402.03318
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
The emergence of organized multiscale patterns resulting from convection is ubiquitous, observed throughout different cloud types. The reproduction of such patterns by general circulation models remains a challenge due to the complex nature of clouds
Externí odkaz:
http://arxiv.org/abs/2304.12199
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