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Assimilating Lagrangian Data in Two-layer systems

Naratip Santitissadeekorn, Chris Jones, Elaine Spiller
University of North Carolina-Chapel Hill
(Abstract received 05/02/2012 for session C)
ABSTRACT

Assimilation of Lagrangian obersvations such as drifter data into fulid models has been found to improve the quality of state estimatations/predictions in many geophysical applications. In this presentation, we investigate the feasibility of Lagrangian data assimilation (LaDA) to estimate the unknown vortex locations on sub-surface layers with the Lagrangian observations on the surface layer in the the two-layer point vortex model. This model exhibits nonlinear features that can fail the standard Kalman filter and its variants. For this reason, we adopt the Monte Carlo approach, known as Particle Filtering, which takes into account of the nonlinear dynamics. Based on this approach, we analyze the quality of the state estimates obtained from assimilating information from different passive tracers.