Results 1 to 10 of about 7,980 (145)

Parameterization of stochastic multiscale triads [PDF]

Nonlinear Processes in Geophysics, 2016
We discuss applications of a recently developed method for model reduction based on linear response theory of weakly coupled dynamical systems. We apply the weak coupling method to simple stochastic differential equations with slow and fast degrees of ...
J. Wouters, S. I. Dolaptchiev, V. Lucarini, U. Achatz   +3 more
doaj   +7 more sources

Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty [PDF]

Nonlinear Processes in Geophysics, 2020
Stochastic subgrid parameterizations enable ensemble forecasts of fluid dynamic systems and ultimately accurate data assimilation (DA). Stochastic advection by Lie transport (SALT) and models under location uncertainty (LU) are recent and similar ...
V. Resseguier, W. Pan, B. Fox-Kemper, B. Fox-Kemper   +3 more
doaj   +1 more source

Scale‐Aware Space‐Time Stochastic Parameterization of Subgrid‐Scale Velocity Enhancement of Sea Surface Fluxes

Journal of Advances in Modeling Earth Systems, 2021
Stochastic representation of the influence of the subgrid‐scales on the resolved scales in weather and climate models has been shown to improve ensemble spread and resolved variability.
Julie Bessac, Hannah M. Christensen, Kota Endo, Adam H. Monahan, Nils Weitzel   +4 more
doaj   +1 more source

Parameterizing the Impact of Unresolved Temperature Variability on the Large‐Scale Density Field: 2. Modeling

Journal of Advances in Modeling Earth Systems, 2022
Ocean circulation models have systematic errors in large‐scale horizontal density gradients due to estimating the grid‐cell‐mean density by applying the nonlinear seawater equation of state to the grid‐cell‐mean water properties. In frontal regions where
J. S. Kenigson, A. Adcroft, S. D. Bachman, F. Castruccio, I. Grooms, P. Pegion, Z. Stanley   +6 more
doaj   +1 more source

Stochastic Parameterization Using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model

Tellus: Series A, Dynamic Meteorology and Oceanography, 2022
Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing.
A. Mukherjee, Y. Aydogdu, T. Ravichandran, N. Sri Namachchivaya   +3 more
doaj   +1 more source

Parameterization of Stochastically Entraining Convection Using Machine Learning Technique

Journal of Advances in Modeling Earth Systems, 2022
A stochastic mixing model with a machine learning technique is proposed for mass flux convection schemes. The model consists of the stochastic differential equations (SDEs) for the fractional entrainment rate, fractional detrainment rate, fractional ...
Jihoon Shin, Jong‐Jin Baik
doaj   +1 more source

Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales [PDF]

Nonlinear Processes in Geophysics, 2020
Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled
M. Van Ginderachter, M. Van Ginderachter, D. Degrauwe, D. Degrauwe, S. Vannitsem, P. Termonia, P. Termonia   +6 more
doaj   +1 more source

Stochastic‐Deep Learning Parameterization of Ocean Momentum Forcing

Journal of Advances in Modeling Earth Systems, 2021
Coupled climate simulations that span several hundred years cannot be run at a high‐enough spatial resolution to resolve mesoscale ocean dynamics. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ...
Arthur P. Guillaumin, Laure Zanna
doaj   +1 more source

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Journal of Advances in Modeling Earth Systems, 2020
Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings.
David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan   +3 more
doaj   +1 more source

Stochastic Data‐Driven Parameterization of Unresolved Eddy Effects in a Baroclinic Quasi‐Geostrophic Model

Journal of Advances in Modeling Earth Systems, 2023
In this work, a stochastic representation based on a physical transport principle is proposed to account for mesoscale eddy effects on the large‐scale oceanic circulation.
Long Li, Bruno Deremble, Noé Lahaye, Etienne Mémin   +3 more
doaj   +1 more source