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| Titel |
Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model |
| VerfasserIn |
S. Skachko, Q. Errera, R. Ménard, Y. Christophe, S. Chabrillat |
| 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 ; 7, no. 4 ; Nr. 7, no. 4 (2014-07-16), S.1451-1465 |
| Datensatznummer |
250115663
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| Publikation (Nr.) |
 copernicus.org/gmd-7-1451-2014.pdf |
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| Zusammenfassung |
| An ensemble Kalman filter (EnKF) assimilation method is applied to the tracer
transport using the same stratospheric transport model as in the four-dimensional variational
(4D-Var) assimilation system BASCOE (Belgian Assimilation
System for Chemical ObsErvations). This EnKF version of BASCOE was built primarily
to avoid the large costs associated with the maintenance of an adjoint model.
The EnKF developed in BASCOE accounts for two adjustable parameters: a
parameter α controlling the model error term and a parameter r
controlling the observational error. The EnKF system is shown to be markedly
sensitive to these two parameters, which are adjusted based on the monitoring
of a χ2 test measuring the misfit between the control variable and the
observations. The performance of the EnKF and 4D-Var versions was estimated
through the assimilation of Aura-MLS (microwave limb sounder) ozone observations during an 8-month
period which includes the formation of the 2008 Antarctic ozone hole. To
ensure a proper comparison, despite the fundamental differences between the
two assimilation methods, both systems use identical and carefully calibrated
input error statistics. We provide the detailed procedure for these
calibrations, and compare the two sets of analyses with a focus on the lower
and middle stratosphere where the ozone lifetime is much larger than the
observational update frequency. Based on the observation-minus-forecast
statistics, we show that the analyses provided by the two systems are
markedly similar, with biases less than 5% and standard deviation
errors less than 10% in most of the stratosphere. Since the biases are
markedly similar, they most probably have the same causes: these can be
deficiencies in the model and in the observation data set, but not in the
assimilation algorithm nor in the error calibration. The remarkably similar
performance also shows that in the context of stratospheric transport, the
choice of the assimilation method can be based on application-dependent
factors, such as CPU cost or the ability to generate an ensemble of
forecasts. |
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