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2002 LAPCOD Meeting
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Assimilation of drifter observations in primitive equation models of midlatitude ocean circulation
Tamay M. Özgökmen(a), Anne Molcard(a,b), Toshio M. Chin(a,c), Leonid I. Piterbarg(d), and Annalisa Griffa(a,b)
(a)RSMAS/MPO, University of Miami, USA, (b)CNR, La Spezia, Italy, (c)Jet Propulsion Laboratory, USA, (d)CAMS, University of Southern California, USA
tozgokmen@rsmas.miami.edu(Abstract received 09/26/2002 for session D)
ABSTRACT
Motivated by increases in the realism of OGCMs and the number of drifting buoys in the ocean observing system, a new Lagrangian assimilation technique developed by Molcard et al. (2002) is implemented in an idealized configuration of the layered primitive equation model MICOM. Using an extensive set of twin experiments, the effectiveness of the Lagrangian observation operator and of a dynamical balancing technique for corrected model variables, which is based on geostrophy and mass conservation, are explored in comparison to a conventional Pseudo-Lagrangian observation operator and a ROIF implementation of the Kalman filter method (Chin et al., 1999). The Pseudo-Lagrangian operator relates the Lagrangian velocity from drifter data to the Eulerian model velocity, whereas the Lagrangian operator relates the Lagrangian velocity from drifter data to the Lagrangian velocity from model-simulated drifters. The Kalman filter is implemented in the Pseudo-Lagrangian mode, and provides a general reference for assimilation performance.
The results clearly illustrate that the Lagrangian observation operator is superior to the Pseudo-Lagrangian in the parameter range that is relevant for typical oceanic drifter observations. The results not only support the validity of the simple dynamical balancing technique, but they also indicate that the correction of model velocity field must be accompanied by an appropriate correction of layer thickness (or pressure, depending on model formulation) for such assimilation to be effective.
Chin, T.M., A.J. Mariano, and E.P. Chassignet, 1999: Spatial regression with Markov Random Fields for Kalman filter approximation in least-squares solution of oceanic data assimilation problems. JGR Oceans, 104, 1233-1257.
Molcard, A., L.I. Piterbarg, A. Griffa, T.M. Ozgokmen, and A.J. Mariano, 2002: Assimilation of drifter observations for the reconstruction of the Eulerian circulation field. JGR Oceans, in press.
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2002 LAPCOD Meeting, Key Largo, Florida, December 12-16, 2000