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State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems Cover

State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

Open Access
|Jan 2018

Figures & Tables

Table 1.

Overview of the sizes of matrices that are used in different filter methods.

VariableEnKFSEIKESSEETKFEAKFEnSRFSEnKFESTKFTNe×NeNe-1×Ne-1N~e×N~eNe×NeNe×NeNe×NeNe×NeNe-1×Ne-1U––Nx×NeNe×NeNe×NeNe×NeNe×NeNe-1×Ne-1Σ––Ne×NeNe×NeNe×NyNe×min(Ne,Ny)Ne×NyNe-1×Ne-1VNy×NyNe×NeNy×Nymin(Ne,Ny)×Ny––LNx×Ne-1–––––Nx×Ne-1ANe×Ne-1–––––Ne×Ne-1
Table 2.

Overview of filter methods available from Sangoma project website.

FilterSectionCommentEnKF5.1includes covariance localisationEnsRF5.6includes covariance localisationETKF/LETKF5.4ETKF is without localisation; LETKF includes domain/observation localisationESTKF/LESTKF5.9ESTKF is without localisation; LESTKF includes domain/observation localisationNETF7.1without localisation
Language: English
Page range: 1445364 - 1445364
Submitted on: Jul 5, 2017
Accepted on: Feb 19, 2018
Published on: Jan 1, 2018
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2018 Sanita Vetra-Carvalho, Peter Jan Van Leeuwen, Lars Nerger, Alexander Barth, M. Umer Altaf, Pierre Brasseur, Paul Kirchgessner, Jean-Marie Beckers, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.