Observation data compression for variational assimilation of dynamical systems (R)
File(s)
Accepted version
Author(s)
Cheng, Sibo
Lucor, Didier
Argaud, Jean-Philippe
Type
Journal Article
Abstract
Accurate estimation of error covariances (both background and observation) is crucial for efficient observation compression approaches in data assimilation of large-scale dynamical problems. We propose a new combination of a covariance tuning algorithm with existing PCA-type data compression approaches, either observation- or information-based, with the aim of reducing the computational cost of real-time updating at each assimilation step. Relying on a local assumption of flow-independent error covariances, dynamical assimilation residuals are used to adjust the covariance in each assimilation window. The estimated covariances then contribute to better specify the principal components of either the observation dynamics or the state-observation sensitivity. The proposed approaches are first validated on a shallow water twin experiment with correlated and non-homogeneous observation error. Proper selection of flow-independent assimilation windows, together with sampling density for background error estimation, and sensitivity of the approaches to the observations error covariance knowledge, are also discussed and illustrated with various numerical tests and results. The method is then applied to a more challenging industrial hydrological model with real-world data and non-linear transformation operator provided by an operational precipitation-flow simulation software.
Date Issued
2021-07-01
Date Acceptance
2021-06-01
Citation
Journal of Computational Science, 2021, 53, pp.1-12
ISSN
1877-7503
Publisher
Elsevier
Start Page
1
End Page
12
Journal / Book Title
Journal of Computational Science
Volume
53
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000674479000004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Computer Science
Data assimilation
Observation compression
Error covariance estimation
Information entropy
Hydrological application
ERROR-STATISTICS
PARAMETERS
DIAGNOSIS
IMPACT
SPACE
MODEL
Publication Status
Published
Article Number
ARTN 101405
Date Publish Online
2021-06-15