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Ann. Geophys., 23, 3171-3185, 2005
www.ann-geophys.net/23/3171/2005/
© European Geosciences Union 2005
Efficient data assimilation into a complex, 3-D physical-biogeochemical model using partially-local Kalman filters
I. Hoteit1, G. Triantafyllou2, and G. Petihakis2
1Scripps Institution of Oceanography, La Jolla, CA, USA
2Hellenic Centre for Marine Research, Anavissos, Greece
Abstract. Advanced Kalman filtering techniques were used to assimilate
pseudo ocean color and profile data into a complex, three-dimensional
coupled physical (POM)-biogeochemical (ERSEM) model of the Cretan Sea
ecosystem. The assimilation schemes, the Singular Evolutive Partially-Local Extended
Kalman (SEPLEK) filter and its variant called SFPLEK, are based on the standard SEEK
filter in which the Kalman correction is made along a set of
"global" and "local" directions, determined via a so-called
"global-local EOF analysis". The global functions are used to
represent the ecosystem large-scale variability. They are allowed to evolve
in time in the SEPLEK filter to follow changes in the model dynamics, while they
remain invariant in the SFPLEK filter. The local functions always remain invariant
and are determined in such a way as to independently represent the different spatial
regimes of the ecological
model. This helps to improve the estimation of fine-scale variations while
requiring significantly less computational time compared to the SEEK filter.
Several assimilation experiments were performed to assess the relevance of
the assimilation system and to study its sensitivity to different choices
of global/local EOFs. The SFPLEK filter was used in all
the sensitivity experiments in order to efficiently measure the representativeness of
the different set of correction directions, as well as to save
computational time. Assimilation results suggest that the use of
global-local correction directions clearly
enhances the filter's performance under different assimilation
setups. The choice of the local directions should, however, be carefully
considered, taking into account the model regional variability and the
characteristics of the observational system.
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