Title :
Assimilation of satellite data in beta-plane ocean circulation models
Author :
Asif, Amir ; Moura, José M F
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
The paper discusses a scheme based on Kalman-Bucy filters for the assimilation of satellite data in equatorial beta plane ocean circulation models. The state equation of the Kalman-Bucy filter is obtained by decoupling the nonlinearities from the Navier-Stokes equations by assuming an inviscid isentropic shallow water motion. Direct application of the Kalman-Bucy filter leads to a computationally intensive algorithm which precludes its application to meaningful sized domains. By imposing a Gauss Markov random field (GMRF) structure on the error covariance matrix, the authors obtain an efficient recursive algorithm, capable of estimating the velocity fields and the sea surface height
Keywords :
Gaussian processes; Kalman filters; Markov processes; Navier-Stokes equations; covariance matrices; digital filters; error analysis; geophysical signal processing; oceanographic techniques; radar altimetry; radar signal processing; random processes; recursive estimation; remote sensing by radar; Gauss Markov random field structure; Kalman-Bucy filters; Navier-Stokes equations; beta-plane ocean circulation models; computationally intensive algorithm; error covariance matrix; inviscid isentropic shallow water motion; nonlinearities; recursive algorithm; satellite data; sea surface height; state equation; velocity fields; Covariance matrix; Filters; Gaussian processes; Markov random fields; Navier-Stokes equations; Nonlinear equations; Oceans; Recursive estimation; Satellites; Sea surface;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.479424