Modelling background error correlations with spatial deformations: a case study

A long-term goal in variational data assimilation is to improve the anisotropy of background error correlations. One way to achieve anisotropic correlations is to introduce spatial deformations. This deformation can be specified a priori for instance by using the geostrophic transform (GT) as introd...

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Main Authors: Raphaël Legrand, Yann Michel
Format: Article
Language:English
Published: Taylor & Francis Group 2014-10-01
Series:Tellus: Series A, Dynamic Meteorology and Oceanography
Subjects:
Online Access:http://www.tellusa.net/index.php/tellusa/article/download/23984/pdf_1
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spelling doaj-9e3d2250ee8a4c6180cc5fbf99dcc24e2020-11-25T00:16:20ZengTaylor & Francis GroupTellus: Series A, Dynamic Meteorology and Oceanography1600-08702014-10-0166011510.3402/tellusa.v66.2398423984Modelling background error correlations with spatial deformations: a case studyRaphaël Legrand0Yann Michel1Météo-France/CNRS CNRM/GAME UMR 3589, 42 avenue Coriolis, FR-31057, Toulouse, FranceMétéo-France/CNRS CNRM/GAME UMR 3589, 42 avenue Coriolis, FR-31057, Toulouse, FranceA long-term goal in variational data assimilation is to improve the anisotropy of background error correlations. One way to achieve anisotropic correlations is to introduce spatial deformations. This deformation can be specified a priori for instance by using the geostrophic transform (GT) as introduced by Desroziers (1997). The deformation can also be estimated from a purely statistical point of view (Michel, 2013a). The aim of this study is to evaluate the performance of such spatial deformation techniques for the use of background error modelling. A large ensemble of variational assimilations with perturbed observations is set up on a case study with the global ARPEGE model. An anisotropy index and a length scale diagnostic are defined to compare objectively the effectiveness of the deformations. This effectiveness is measured as the ability of the inverse spatial deformations to make the correlations more isotropic or more homogeneous. The results are shown to depend on the vertical level and on the variable. Generally, the statistical deformation is able to reduce the anisotropy while the GT is giving much smaller improvements that are, in this case study, confined to the frontal area of an extratropical cyclone.http://www.tellusa.net/index.php/tellusa/article/download/23984/pdf_1data assimilationanisotropybackground errorensemblespatial deformation
collection DOAJ
language English
format Article
sources DOAJ
author Raphaël Legrand
Yann Michel
spellingShingle Raphaël Legrand
Yann Michel
Modelling background error correlations with spatial deformations: a case study
Tellus: Series A, Dynamic Meteorology and Oceanography
data assimilation
anisotropy
background error
ensemble
spatial deformation
author_facet Raphaël Legrand
Yann Michel
author_sort Raphaël Legrand
title Modelling background error correlations with spatial deformations: a case study
title_short Modelling background error correlations with spatial deformations: a case study
title_full Modelling background error correlations with spatial deformations: a case study
title_fullStr Modelling background error correlations with spatial deformations: a case study
title_full_unstemmed Modelling background error correlations with spatial deformations: a case study
title_sort modelling background error correlations with spatial deformations: a case study
publisher Taylor & Francis Group
series Tellus: Series A, Dynamic Meteorology and Oceanography
issn 1600-0870
publishDate 2014-10-01
description A long-term goal in variational data assimilation is to improve the anisotropy of background error correlations. One way to achieve anisotropic correlations is to introduce spatial deformations. This deformation can be specified a priori for instance by using the geostrophic transform (GT) as introduced by Desroziers (1997). The deformation can also be estimated from a purely statistical point of view (Michel, 2013a). The aim of this study is to evaluate the performance of such spatial deformation techniques for the use of background error modelling. A large ensemble of variational assimilations with perturbed observations is set up on a case study with the global ARPEGE model. An anisotropy index and a length scale diagnostic are defined to compare objectively the effectiveness of the deformations. This effectiveness is measured as the ability of the inverse spatial deformations to make the correlations more isotropic or more homogeneous. The results are shown to depend on the vertical level and on the variable. Generally, the statistical deformation is able to reduce the anisotropy while the GT is giving much smaller improvements that are, in this case study, confined to the frontal area of an extratropical cyclone.
topic data assimilation
anisotropy
background error
ensemble
spatial deformation
url http://www.tellusa.net/index.php/tellusa/article/download/23984/pdf_1
work_keys_str_mv AT raphaellegrand modellingbackgrounderrorcorrelationswithspatialdeformationsacasestudy
AT yannmichel modellingbackgrounderrorcorrelationswithspatialdeformationsacasestudy
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