Abstract
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)
Highlights
Data scarcity and model inaccuracies are at the base of the limited predictability of oceanic systems
We can distinguish between the methods traditionally implemented for linear model dynamics, e.g. the Kalman Filter (see Kalman (1960) and Kalman and Bucy (1961) for the original works) and the adjoint technique, first properly introduced to oceanography and meteorology by Talagrand and Courtier (1987) and Courtier and Talagrand (1987)
After the onset of stratification, a nutricline forms just below the deep chlorophyll maxima (DCM), which is eroded as the DCM deepens
Summary
Data scarcity and model inaccuracies are at the base of the limited predictability of oceanic systems. We can distinguish between the methods traditionally implemented for linear model dynamics, e.g. the Kalman Filter (see Kalman (1960) and Kalman and Bucy (1961) for the original works) and the adjoint technique, first properly introduced to oceanography and meteorology by Talagrand and Courtier (1987) and Courtier and Talagrand (1987). The application of these techniques to work with nonlinear model dynamics has met with variable success. There has been the development of significant new assimilation formulations and techniques which have been tailored towards
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