Eigenvector-spatial localisation

We present a new multiscale covariance localisation method for ensemble data assimilation that is based on the estimation of eigenvectors and subsequent projections, together with traditional spatial localisation applied with a range of localisation lengths. In short, we estimate the leading, large-...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Travis Harty, Matthias Morzfeld, Chris Snyder
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	localisation multiscale data assimilation kalman filter ensemble Oceanography GC1-1581 Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/3ec824684f74427ea6acc3bcc9395ff4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3ec824684f74427ea6acc3bcc9395ff4
record_format	dspace
spelling	oai:doaj.org-article:3ec824684f74427ea6acc3bcc9395ff42021-12-01T14:40:58ZEigenvector-spatial localisation1600-087010.1080/16000870.2021.1903692https://doaj.org/article/3ec824684f74427ea6acc3bcc9395ff42021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/16000870.2021.1903692https://doaj.org/toc/1600-0870We present a new multiscale covariance localisation method for ensemble data assimilation that is based on the estimation of eigenvectors and subsequent projections, together with traditional spatial localisation applied with a range of localisation lengths. In short, we estimate the leading, large-scale eigenvectors from the sample covariance matrix obtained by spatially smoothing the ensemble (treating small scales as noise) and then localise the resulting sample covariances with a large length scale. After removing the projection of each ensemble member onto the leading eigenvectors, the process may be repeated using less smoothing and tighter localizations or, in a final step, using the resulting, residual ensemble and tight localisation to represent covariances in the remaining subspace. We illustrate the use of the new multiscale localisation method in simple numerical examples and in cycling data assimilation experiments with the Lorenz Model III. We also compare the proposed new method to existing multiscale localisation and to single-scale localisation.Travis HartyMatthias MorzfeldChris SnyderTaylor & Francis Grouparticlelocalisationmultiscaledata assimilationkalman filterensembleOceanographyGC1-1581Meteorology. ClimatologyQC851-999ENTellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	localisation multiscale data assimilation kalman filter ensemble Oceanography GC1-1581 Meteorology. Climatology QC851-999
spellingShingle	localisation multiscale data assimilation kalman filter ensemble Oceanography GC1-1581 Meteorology. Climatology QC851-999 Travis Harty Matthias Morzfeld Chris Snyder Eigenvector-spatial localisation
description	We present a new multiscale covariance localisation method for ensemble data assimilation that is based on the estimation of eigenvectors and subsequent projections, together with traditional spatial localisation applied with a range of localisation lengths. In short, we estimate the leading, large-scale eigenvectors from the sample covariance matrix obtained by spatially smoothing the ensemble (treating small scales as noise) and then localise the resulting sample covariances with a large length scale. After removing the projection of each ensemble member onto the leading eigenvectors, the process may be repeated using less smoothing and tighter localizations or, in a final step, using the resulting, residual ensemble and tight localisation to represent covariances in the remaining subspace. We illustrate the use of the new multiscale localisation method in simple numerical examples and in cycling data assimilation experiments with the Lorenz Model III. We also compare the proposed new method to existing multiscale localisation and to single-scale localisation.
format	article
author	Travis Harty Matthias Morzfeld Chris Snyder
author_facet	Travis Harty Matthias Morzfeld Chris Snyder
author_sort	Travis Harty
title	Eigenvector-spatial localisation
title_short	Eigenvector-spatial localisation
title_full	Eigenvector-spatial localisation
title_fullStr	Eigenvector-spatial localisation
title_full_unstemmed	Eigenvector-spatial localisation
title_sort	eigenvector-spatial localisation
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/3ec824684f74427ea6acc3bcc9395ff4
work_keys_str_mv	AT travisharty eigenvectorspatiallocalisation AT matthiasmorzfeld eigenvectorspatiallocalisation AT chrissnyder eigenvectorspatiallocalisation
_version_	1718404986482720768

Eigenvector-spatial localisation

Ejemplares similares