Ensemble Kalman filters for dynamical systems with unresolved turbulence

DOI10.1016/j.jcp.2014.05.037zbMath1351.76031OpenAlexW2007384560WikidataQ61785909 ScholiaQ61785909MaRDI QIDQ728600

Yoonsang Lee, Ian Grooms, Andrew J. Majda

Publication date: 20 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2014.05.037

zbMATH Keywords

ensemble Kalman filtersuperparameterizationrepresentation errorturbulent dynamics

Mathematics Subject Classification ID

Hydrology, hydrography, oceanography (86A05) Meteorology and atmospheric physics (86A10) Computational methods for problems pertaining to geophysics (86-08) Dynamical systems approach to turbulence (76F20)

Related Items

Accuracy of Some Approximate Gaussian Filters for the Navier--Stokes Equation in the Presence of Model Error, A state estimation approach based on stochastic expansions, Stochastic Superparameterization and Multiscale Filtering of Turbulent Tracers, An information-theoretic framework for improving imperfect dynamical predictions via multi-model ensemble forecasts, Multiscale Methods for Data Assimilation in Turbulent Systems, Recovering the Eulerian energy spectrum from noisy Lagrangian tracers, Blended particle methods with adaptive subspaces for filtering turbulent dynamical systems

Uses Software

• CRCP

Cites Work

• Dynamic Stochastic Superresolution of sparsely observed turbulent systems
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• Dispersive wave turbulence in one dimension