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| Titel |
Impact of a time-dependent background error covariance matrix on air quality analysis |
| VerfasserIn |
E. Jaumouillé, S. Massart, A. Piacentini, D. Cariolle, V.-H. Peuch |
| 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 ; 5, no. 5 ; Nr. 5, no. 5 (2012-09-06), S.1075-1090 |
| Datensatznummer |
250002842
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| Publikation (Nr.) |
 copernicus.org/gmd-5-1075-2012.pdf |
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| Zusammenfassung |
| In this article we study the influence of different characteristics of our assimilation system
on surface ozone analyses over Europe. Emphasis is placed on the evaluation of the background error covariance matrix (BECM).
Data assimilation systems require a BECM in order to obtain an optimal representation of the physical state. A posteriori diagnostics
are an efficient way to check the consistency of the used BECM. In this study we derived a diagnostic to estimate the BECM. On the other
hand, an increasingly used approach to obtain such a covariance matrix is to estimate it from an ensemble of perturbed assimilation experiments.
We applied this method, combined with variational assimilation, while analysing the surface ozone distribution over Europe. We first show
that the resulting covariance matrix is strongly time (hourly and seasonally) and space dependent. We then built several configurations
of the background error covariance matrix with none, one or two of its components derived from the ensemble estimation. We used each
of these configurations to produce surface ozone analyses. All the analyses are compared between themselves and compared to assimilated
data or data from independent validation stations. The configurations are very well correlated with the validation stations, but
with varying regional and seasonal characteristics. The largest correlation is obtained with the experiments using time- and
space-dependent correlation of the background errors. Results show that our assimilation process is efficient in bringing the model
assimilations closer to the observations than the direct simulation, but we cannot conclude which BECM configuration is the
best. The impact of the background error covariances configuration on four-days forecasts is also studied. Although mostly
positive, the impact depends on the season and lasts longer during the winter season. |
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