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| Titel |
An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea |
| VerfasserIn |
J. I. Allen, M. Eknes, G. Evensen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 21, no. 1 ; Nr. 21, no. 1, S.399-411 |
| Datensatznummer |
250014565
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| Publikation (Nr.) |
 copernicus.org/angeo-21-399-2003.pdf |
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| Zusammenfassung |
The purpose of this paper
is to examine the use of a complex ecosystem model along with near real-time in
situ data and a sequential data assimilation method for state estimation. The
ecosystem model used is the European Regional Seas Ecosystem Model (ERSEM;
Baretta et al., 1995) and the assimilation method chosen is the Ensemble Kalman
Filer (EnKF). Previously, it has been shown that this method captures the
nonlinear error evolution in time and is capable of both tracking the
observations and providing realistic error estimates for the estimated state.
This system has been used to assimilate long time series of in situ chlorophyll
taken from a data buoy in the Cretan Sea. The assimilation of this data using
the EnKF method results in a marked improvement in the ability of ERSEM to
hindcast chlorophyll. The sensitivity of this system to the type of data used
for assimilation, the frequency of assimilation, ensemble size and model errors
is discussed. The predictability window of the EnKF appears to be at least 2
days. This is an indication that the methodology might be suitable for future
operational data assimilation systems using more complex three-dimensional
models.
Key words. Oceanography: general
(numerical modelling; ocean prediction) – Oceanography: biological and
chemical (plankton) |
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