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A Multiscale Variational Data Assimilation Scheme: Formulation and Illustration

Li, Zhijin ; McWilliams, James C ; Ide, Kayo ; Farrara, John D

Monthly weather review, 2015-09, Vol.143 (9), p.3804-3822 [Periódico revisado por pares]

Washington: American Meteorological Society

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  • Título:
    A Multiscale Variational Data Assimilation Scheme: Formulation and Illustration
  • Autor: Li, Zhijin ; McWilliams, James C ; Ide, Kayo ; Farrara, John D
  • Assuntos: Assimilation ; Climatology ; Cost function ; Covariance ; Data assimilation ; Decomposition ; Fine structure ; Meteorology ; Permissible error ; Remote sensing ; Weather
  • É parte de: Monthly weather review, 2015-09, Vol.143 (9), p.3804-3822
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
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  • Descrição: A multiscale data assimilation (MS-DA) scheme is formulated for fine-resolution models. A decomposition of the cost function is derived for a set of distinct spatial scales. The decomposed cost function allows for the background error covariance to be estimated separately for the distinct spatial scales, and multi-decorrelation scales to be explicitly incorporated in the background error covariance. MS-DA minimizes the partitioned cost functions sequentially from large to small scales. The multi-decorrelation length scale background error covariance enhances the spreading of sparse observations and prevents fine structures in high-resolution observations from being overly smoothed. The decomposition of the cost function also provides an avenue for mitigating the effects of scale aliasing and representativeness errors that inherently exist in a multiscale system, thus further improving the effectiveness of the assimilation of high-resolution observations. A set of one-dimensional experiments is performed to examine the properties of the MS-DA scheme. Emphasis is placed on the assimilation of patchy high-resolution observations representing radar and satellite measurements, alongside sparse observations representing those from conventional in situ platforms. The results illustrate how MS-DA improves the effectiveness of the assimilation of both these types of observations simultaneously.
  • Editor: Washington: American Meteorological Society
  • Idioma: Inglês
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