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| Titel |
Parameter estimation in an atmospheric GCM using the Ensemble Kalman Filter |
| VerfasserIn |
J. D. Annan  , D. J. Lunt, J. C. Hargreaves, P. J. Valdes |
| 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 ; 12, no. 3 ; Nr. 12, no. 3 (2005-02-25), S.363-371 |
| Datensatznummer |
250010592
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| Publikation (Nr.) |
 copernicus.org/npg-12-363-2005.pdf |
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| Zusammenfassung |
| We demonstrate the application of an efficient multivariate probabilistic
parameter estimation method to a spectral primitive equation atmospheric GCM. The method,
which is based on the Ensemble Kalman Filter, is effective at tuning the
surface air temperature climatology of the model
to both identical twin data and reanalysis data. When 5 parameters were simultaneously tuned to fit the model to reanalysis data, the model errors were
reduced by around 35% compared
to those given by the default parameter values. However, the precipitation field proved to be insensitive to these parameters and remains rather poor.
The model is computationally cheap but chaotic and otherwise
realistic, and the success of these experiments suggests that this
method should be capable of tuning more sophisticated models, in
particular for the purposes of climate hindcasting and
prediction. Furthermore, the
method is shown to be useful in determining structural
deficiencies in the model
which can not be improved by tuning, and so can be a useful
tool to guide model development. The work presented here is for a
limited set of parameters and data,
but the scalability of the method is such
that it could easily be extended to a more comprehensive parameter set given
sufficient observational data to constrain them. |
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