Abstract
A demonstration study of three advanced, sequential data assimilation methods, applied with the nonlinear Miami Isopycnic Coordinate Ocean Model (MICOM), has been performed within the European Commission-funded DIADEM project. The data assimilation techniques considered are the Ensemble Kalman Filter (EnKF), the Ensemble Kalman Smoother (EnKS) and the Singular Evolutive Extended Kalman (SEEK) Filter, which all in different ways resemble the original Kalman Filter. In the EnKF and EnKS an ensemble of model states is integrated forward in time according to the model dynamics, and statistical moments needed at analysis time are calculated from the ensemble of model states. The EnKS, as opposed to the EnKF, update the analysis also backward in time whenever new observations are available, thereby improving the estimated states at the previous analysis times. The SEEK filter reduces the computational burden of the error propagation by representing the errors in a subspace which is initially calculated from a truncated EOF analysis. A hindcast experiment, where sea-level anomaly and sea-surface temperature data are assimilated, has been conducted in the North Atlantic for the time period July until September 1996. In this paper, we describe the implementation of ensemble-based assimilation methods with a common theoretical framework, we present results from hindcast experiments achieved with the EnKF, EnKS and SEEK filter, and we discuss the relative merits of these methods from the perspective of operational marine monitoring and forecasting systems. We found that the three systems have similar performances, and they can be considered feasible technologically for building preoperational prototypes.
Highlights
An ocean monitoring and prediction system must rely on integrated use of available remotely sensed and in situ measured observations together with dynamical models to achieve a best possible estimation of the true state of the ocean. Such integrated use of observations and model tools is best done using socalled data assimilation methods which provide a mean for optimal combination of the information about the real world contained in observations and the information about dynamical processes described by the models
The data assimilation methods considered are the Ensemble Kalman Filter (EnKF) by Evensen (1994), the Ensemble Kalman Smoother (EnKS) by Evensen and van Leeuwen (2000) and the Singular Evolutive Extended Kalman (SEEK) filter by Pham et al (1998), which all are based on dynamically consistent estimates of the model error statistics
We have observed that this improves the analysis since the size of the ensemble in the EnKF, or the error subspace in the SEEK, relative to the number of state variables at a particular grid-point is of the same order and, span a larger part of the model state space
Summary
An ocean monitoring and prediction system must rely on integrated use of available remotely sensed and in situ measured observations together with dynamical models to achieve a best possible estimation of the true state of the ocean. Such integrated use of observations and model tools is best done using socalled data assimilation methods which provide a mean for optimal combination of the information about the real world contained in observations and the information about dynamical processes described by the models. The first guess for the EnKS equals the EnKF solution, and subsequent smoother estimates are improvements of the first guess solution
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