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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 1
Ann. Geophys., 21, 399–411, 2003
https://doi.org/10.5194/angeo-21-399-2003
© Author(s) 2003. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Mediterranean Forecasting System Pilot Project (MFSPP)

Ann. Geophys., 21, 399–411, 2003
https://doi.org/10.5194/angeo-21-399-2003
© Author(s) 2003. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jan 2003

31 Jan 2003

An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea

J. I. Allen1, M. Eknes2, and G. Evensen2 J. I. Allen et al.
  • 1Plymouth Marine Laboratory, Prospect Place, West Hoe, Plymouth, PL1 3DH, UK
  • 2Nansen Environmental and Remote Sensing Centre, Edvard Griegsvei 3a, N-5037 Solheimsviken, Norway

Abstract. 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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