Abstract
Dynamical evolution of the error statistics with the SEEK filter to assimilate altimetric data in eddy-resolving ocean models
Highlights
During the last decade, the observation of the ocean from space has led to an unprecedented amount of oceanographic data
The Singular Evolutive Extended Kalman (SEEK) filter introduced by Pham et al (1998) is one of these simplifications
The first set of empirical orthogonal functions (EOFs) accounts for the variance of the model during the time period corresponding to the synthetic observations to be assimilated
Summary
The observation of the ocean from space has led to an unprecedented amount of oceanographic data. Only r eigenvectors are needed on the assimilation algorithm instead of the full covariance matrix This hypothesis is equivalent to the condition of order reduction, in which the state vector may be expressed as (4). In the context of a sequential scheme, Cane et al (1996) project the KF equations onto an EOF basis in order to construct an analog of the Kalman filter for the principal components In their application, the time evolution of the principal components of a small perturbation of the state vector, ax, is computed as follows:. In the SEEK filter, Pham et al (1998) take a different approach to Eq (5), by allowing the dynamical model to modify the directions defined by the columns of ST, i.e. i.e. the linear evolution of the current state of the system is expressed by the evolution of each one of the members of the subspace basis.
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