Assimilation of Lagrangian data in oceanography

Variational assimilation of Lagrangian data in oceanography

We consider the assimilation of Lagrangian data into a primitive equation circulation model of the ocean at basin scale. The Lagrangian data are positions of floats drifting at fixed depth. We aim at reconstructing the fourdimensional space-time circulation of the ocean. This problem is solved using the four-dimensional variational technique and the adjoint method. In this problem the control v...

Sequential data assimilation techniques in oceanography

We review recent developments of sequential data assimilation techniques used in oceanography to integrate spatio-temporal observations into numerical models describing physical and ecological dynamics. Theoretical aspects from the simple case of linear dynamics to the general case of nonlinear dynamics are described from a geostatistical point-of-view. Current methods derived from the Kalman f...

Status and future of data assimilation in operational oceanography

MJ Martin, Met Office, Exeter, UK. M Balmaseda, ECMWF, Reading, UK L Bertino, NERSC, Bergen, Norway P Brasseur, LEGI, Grenoble, France G Brassington, Bureau of Meteorology, Melbourne, Australia J Cummings, NRL, Monterey, USA Y Fujii, JMA/MRI, Tsukuba, Japan DJ Lea, Met Office, Exeter, UK J-M Lellouche, Mercator Ocean, Toulouse, France K Mogensen, ECMWF, Reading, UK P Oke, CSIRO, Hobart, Austral...

Assimilation of Lagrangian Data in NEMOVAR

Parallel filter algorithms for data assimilation in oceanography

A consistent systematic comparison of filter algorithms based on the Kalman filter and intended for data assimilation with high-dimensional nonlinear numerical models is presented. Considered are the Ensemble Kalman Filter (EnKF), the Singular Evolutive Extended Kalman (SEEK) filter, and the Singular Evolutive Interpolated (SEIK) filter. Within the two parts of this thesis, the filter algorithm...