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Modelling background error correlations with spatial deformations: a case study

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Title: Modelling background error correlations with spatial deformations: a case study
Authors: Raphaël Legrand, Yann Michel
Source: Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 66, Iss 0, Pp 1-15 (2014)
Publisher Information: Stockholm University Press, 2014.
Publication Year: 2014
Collection: LCC:Oceanography
LCC:Meteorology. Climatology
Subject Terms: data assimilation, anisotropy, background error, ensemble, spatial deformation, Oceanography, GC1-1581, Meteorology. Climatology, QC851-999
More Details: 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1600-0870
Relation: http://www.tellusa.net/index.php/tellusa/article/download/23984/pdf_1; https://doaj.org/toc/1600-0870
DOI: 10.3402/tellusa.v66.23984
Access URL: https://doaj.org/article/9e3d2250ee8a4c6180cc5fbf99dcc24e
Accession Number: edsdoj.9e3d2250ee8a4c6180cc5fbf99dcc24e
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  Data: Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 66, Iss 0, Pp 1-15 (2014)
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  Data: Stockholm University Press, 2014.
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  Data: <searchLink fieldCode="DE" term="%22data+assimilation%22">data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22anisotropy%22">anisotropy</searchLink><br /><searchLink fieldCode="DE" term="%22background+error%22">background error</searchLink><br /><searchLink fieldCode="DE" term="%22ensemble%22">ensemble</searchLink><br /><searchLink fieldCode="DE" term="%22spatial+deformation%22">spatial deformation</searchLink><br /><searchLink fieldCode="DE" term="%22Oceanography%22">Oceanography</searchLink><br /><searchLink fieldCode="DE" term="%22GC1-1581%22">GC1-1581</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorology%2E+Climatology%22">Meteorology. Climatology</searchLink><br /><searchLink fieldCode="DE" term="%22QC851-999%22">QC851-999</searchLink>
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  Data: 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.
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        Value: 10.3402/tellusa.v66.23984
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      – Text: English
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      Pagination:
        PageCount: 15
        StartPage: 1
    Subjects:
      – SubjectFull: data assimilation
        Type: general
      – SubjectFull: anisotropy
        Type: general
      – SubjectFull: background error
        Type: general
      – SubjectFull: ensemble
        Type: general
      – SubjectFull: spatial deformation
        Type: general
      – SubjectFull: Oceanography
        Type: general
      – SubjectFull: GC1-1581
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      – SubjectFull: Meteorology. Climatology
        Type: general
      – SubjectFull: QC851-999
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      – TitleFull: Modelling background error correlations with spatial deformations: a case study
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