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| Titel |
A framework for variational data assimilation with superparameterization |
| VerfasserIn |
I. Grooms, Y. Lee |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 5 ; Nr. 22, no. 5 (2015-10-09), S.601-611 |
| Datensatznummer |
250121003
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| Publikation (Nr.) |
 copernicus.org/npg-22-601-2015.pdf |
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| Zusammenfassung |
| Superparameterization (SP) is a multiscale computational approach wherein a
large scale atmosphere or ocean model is coupled to an array of simulations
of small scale dynamics on periodic domains embedded into the computational
grid of the large scale model. SP has been successfully developed in global
atmosphere and climate models, and is a promising approach for new
applications, but there is currently no practical data assimilation framework
that can be used with these models. The authors develop a 3D-Var variational
data assimilation framework for use with SP; the relatively low cost and
simplicity of 3D-Var in comparison with ensemble approaches makes it a
natural fit for relatively expensive multiscale SP models. To demonstrate the
assimilation framework in a simple model, the authors develop a new system of
ordinary differential equations similar to the two-scale Lorenz-'96 model.
The system has one set of variables denoted {Yi}, with large and small
scale parts, and the SP approximation to the system is straightforward. With
the new assimilation framework the SP model approximates the large scale
dynamics of the true system accurately. |
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