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| Titel |
Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models |
| VerfasserIn |
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Zabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San José, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, C. Seigneur |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 15, no. 10 ; Nr. 15, no. 10 (2015-05-18), S.5325-5358 |
| Datensatznummer |
250119728
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| Publikation (Nr.) |
 copernicus.org/acp-15-5325-2015.pdf |
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| Zusammenfassung |
| Data assimilation is used in atmospheric chemistry models to improve air
quality forecasts, construct re-analyses of three-dimensional chemical
(including aerosol) concentrations and perform inverse modeling of input
variables or model parameters (e.g., emissions). Coupled chemistry
meteorology models (CCMM) are atmospheric chemistry models that simulate
meteorological processes and chemical transformations jointly. They offer
the possibility to assimilate both meteorological and chemical data;
however, because CCMM are fairly recent, data assimilation in CCMM has been
limited to date. We review here the current status of data assimilation in
atmospheric chemistry models with a particular focus on future prospects for
data assimilation in CCMM. We first review the methods available for data
assimilation in atmospheric models, including variational methods, ensemble
Kalman filters, and hybrid methods. Next, we review past applications that
have included chemical data assimilation in chemical transport models (CTM)
and in CCMM. Observational data sets available for chemical data
assimilation are described, including surface data, surface-based remote
sensing, airborne data, and satellite data. Several case studies of chemical
data assimilation in CCMM are presented to highlight the benefits obtained
by assimilating chemical data in CCMM. A case study of data assimilation to
constrain emissions is also presented. There are few examples to date of
joint meteorological and chemical data assimilation in CCMM and potential
difficulties associated with data assimilation in CCMM are discussed. As the
number of variables being assimilated increases, it is essential to
characterize correctly the errors; in particular, the specification of error
cross-correlations may be problematic. In some cases, offline diagnostics
are necessary to ensure that data assimilation can truly improve model
performance. However, the main challenge is likely to be the paucity of
chemical data available for assimilation in CCMM. |
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