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| Titel |
Construction of non-diagonal background error covariance matrices for global chemical data assimilation |
| VerfasserIn |
K. Singh, M. Jardak, A. Sandu, K. Bowman, M. Lee, D. Jones |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 4, no. 2 ; Nr. 4, no. 2 (2011-04-11), S.299-316 |
| Datensatznummer |
250001655
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| Publikation (Nr.) |
 copernicus.org/gmd-4-299-2011.pdf |
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| Zusammenfassung |
| Chemical data assimilation attempts to optimally use noisy observations along
with imperfect model predictions to produce a better estimate of the chemical
state of the atmosphere. It is widely accepted that a key ingredient for
successful data assimilation is a realistic estimation of the background
error distribution. Particularly important is the specification of the
background error covariance matrix, which contains information about the
magnitude of the background errors and about their correlations. As models
evolve toward finer resolutions, the use of diagonal background covariance
matrices is increasingly inaccurate, as they captures less of the spatial
error correlations. This paper discusses an efficient computational procedure
for constructing non-diagonal background error covariance matrices which
account for the spatial correlations of errors. The correlation length scales
are specified by the user; a correct choice of correlation lengths is
important for a good performance of the data assimilation system. The
benefits of using the non-diagonal covariance matrices for variational data
assimilation with chemical transport models are illustrated. |
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