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| Titel |
Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation |
| VerfasserIn |
G. Candille, J.-M. Brankart, P. Brasseur |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1812-0784
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| Digitales Dokument |
URL |
| Erschienen |
In: Ocean Science ; 11, no. 3 ; Nr. 11, no. 3 (2015-06-04), S.425-438 |
| Datensatznummer |
250117226
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| Publikation (Nr.) |
 copernicus.org/os-11-425-2015.pdf |
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| Zusammenfassung |
A realistic circulation model of the North Atlantic ocean at 0.25°
resolution (NATL025 NEMO configuration) has been adapted to explicitly
simulate model uncertainties. This is achieved by introducing stochastic
perturbations in the equation of state to represent the effect of unresolved
scales on the model dynamics. The main motivation for this work is to develop
ensemble data assimilation methods, assimilating altimetric data from past
missions Jason-1 and Envisat. The assimilation experiment is designed to
provide a description of the uncertainty associated with the Gulf Stream
circulation for years 2005/2006, focusing on frontal regions which are
predominantly affected by unresolved dynamical scales. An ensemble based on
such stochastic perturbations is first produced and evaluated using
along-track altimetry observations. Then each ensemble member is updated by a
square root algorithm based on the SEEK (singular evolutive extended Kalman) filter (Brasseur and Verron,
2006). These
three elements – stochastic parameterization, ensemble simulation and 4-D
observation operator – are then used together to perform a 4-D analysis of
along-track altimetry over 10-day windows. Finally, the results of this
experiment are objectively evaluated using the standard probabilistic
approach developed for meteorological applications (Toth et al., 2003; Candille et al., 2007).
The results show that the free ensemble – before starting the assimilation
process – correctly reproduces the statistical variability over the Gulf
Stream area: the system is then pretty reliable but not informative (null
probabilistic resolution). Updating the free ensemble with altimetric data
leads to a better reliability with an information gain of around 30% (for
10-day forecasts of the SSH variable). Diagnoses on fully independent data
(i.e. data that are not assimilated, like temperature and salinity
profiles) provide more contrasted results when the free and updated ensembles
are compared. |
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