An anisotropic formulation of the parametric Kalman filter assimilation

In geophysics, the direct application of covariance matrix dynamics described by the Kalman filter (KF) is limited by the high dimension of such problems. The parametric Kalman filter (PKF) is a recent alternative to the ensemble Kalman filter, where the covariance matrices are approximated by a cov...

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Autor principal:	Olivier Pannekoucke
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	data assimilation parametric kalman filter univariate analysis multivariate analysis Oceanography GC1-1581 Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/e18e4a3c6d874b82a78ba97778e0a390
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id	oai:doaj.org-article:e18e4a3c6d874b82a78ba97778e0a390
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spelling	oai:doaj.org-article:e18e4a3c6d874b82a78ba97778e0a3902021-12-01T14:40:58ZAn anisotropic formulation of the parametric Kalman filter assimilation1600-087010.1080/16000870.2021.1926660https://doaj.org/article/e18e4a3c6d874b82a78ba97778e0a3902021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/16000870.2021.1926660https://doaj.org/toc/1600-0870In geophysics, the direct application of covariance matrix dynamics described by the Kalman filter (KF) is limited by the high dimension of such problems. The parametric Kalman filter (PKF) is a recent alternative to the ensemble Kalman filter, where the covariance matrices are approximated by a covariance model featured by a set of parameters. The covariance dynamics is then described by the time evolution of these parameters during the analysis and forecast cycles. This study focuses on covariance model parametrized by the variance and the local anisotropic tensor fields (VLATcov). The analysis step of the PKF for VLATcov in a 2D/3D domain is first introduced. Then, using 2D univariate numerical investigations, the PKF is shown to be able to provide a low numerical cost approximation of the Kalman filter analysis step, even for anisotropic error correlation functions. Moreover the PKF has been shown able to reproduce the KF over several assimilation cycles in a transport dynamics. An extension toward the multivariate situation is theoretically studied in a 1D domain.Olivier PannekouckeTaylor & Francis Grouparticledata assimilationparametric kalman filterunivariate analysismultivariate analysisOceanographyGC1-1581Meteorology. ClimatologyQC851-999ENTellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-27 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data assimilation parametric kalman filter univariate analysis multivariate analysis Oceanography GC1-1581 Meteorology. Climatology QC851-999
spellingShingle	data assimilation parametric kalman filter univariate analysis multivariate analysis Oceanography GC1-1581 Meteorology. Climatology QC851-999 Olivier Pannekoucke An anisotropic formulation of the parametric Kalman filter assimilation
description	In geophysics, the direct application of covariance matrix dynamics described by the Kalman filter (KF) is limited by the high dimension of such problems. The parametric Kalman filter (PKF) is a recent alternative to the ensemble Kalman filter, where the covariance matrices are approximated by a covariance model featured by a set of parameters. The covariance dynamics is then described by the time evolution of these parameters during the analysis and forecast cycles. This study focuses on covariance model parametrized by the variance and the local anisotropic tensor fields (VLATcov). The analysis step of the PKF for VLATcov in a 2D/3D domain is first introduced. Then, using 2D univariate numerical investigations, the PKF is shown to be able to provide a low numerical cost approximation of the Kalman filter analysis step, even for anisotropic error correlation functions. Moreover the PKF has been shown able to reproduce the KF over several assimilation cycles in a transport dynamics. An extension toward the multivariate situation is theoretically studied in a 1D domain.
format	article
author	Olivier Pannekoucke
author_facet	Olivier Pannekoucke
author_sort	Olivier Pannekoucke
title	An anisotropic formulation of the parametric Kalman filter assimilation
title_short	An anisotropic formulation of the parametric Kalman filter assimilation
title_full	An anisotropic formulation of the parametric Kalman filter assimilation
title_fullStr	An anisotropic formulation of the parametric Kalman filter assimilation
title_full_unstemmed	An anisotropic formulation of the parametric Kalman filter assimilation
title_sort	anisotropic formulation of the parametric kalman filter assimilation
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/e18e4a3c6d874b82a78ba97778e0a390
work_keys_str_mv	AT olivierpannekoucke ananisotropicformulationoftheparametrickalmanfilterassimilation AT olivierpannekoucke anisotropicformulationoftheparametrickalmanfilterassimilation
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An anisotropic formulation of the parametric Kalman filter assimilation

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