HR: 08:50h
AN: NG31C-04 INVITED     [Abstracts]
TI: Assimilation of Lagrangian Data for Estimation and Forecast of Coherent Structures and Transport in Geophysical Flows
AU: * Ide, K
EM: kayo@atmos.ucla.edu
AF: University of California, Los Angeles, P.O. Box 951567, Los Angeles, CA 90095-1567 United States
AU: Jones, C K
EM: ckrtj@email.unc.edu
AF: University of North Carolina at Chapel Hill, CB 3250 Phillips Hall, Chapel Hill, NC 27599-3250 United States
AB: Various manifestations of Lagrangian dynamics exist in the geophysical systems and their signatures may arise in distinct forms at a wide range of spatial scales. Capturing these Lagrangian signatures helps us enhance our predictive capability using the Eulerian model. In the macroscopic form, the Lagrangian signatures may be visible as coherent structures in the sequence of Eulerian synoptic fields. They are dynamically active and often identified as the regions of conserved properties with sharp gradients along the boundaries. These boundaries may be dynamically less active but are important because they govern the mixing process between the interior and exterior. In the microscopic form, the Lagrangian signature is manifest in the trajectory of a traceable marker which can be an observation instrument. The primary attributes of Lagrangian information obtained by the instruments are the ability to trace large-scale coherent structures on one hand and display small-scale mixing process on the other hand, while satisfying the conservation laws for many observed properties the along the trajectory. Lagrangian information thus offers unique perspectives of the circulation. We develop a comprehensive data assimilation platform with a focus on estimation of coherent structures in the Eulerian model through assimilation of Lagrangian data. Recent developments in the use of dynamical systems theory have brought a new aspect of Lagrangian analysis to the geophysical flow dynamics. It offers an algorithm to detect the unseen boundaries of the coherent structures as a non-local material curves originating from the relevant hyperbolic trajectories. Our data assimilation platform takes advantage of the dynamical systems theory. Targeting these hyperbolic trajectories naturally leads to an adaptive observing system.
DE: 3337 Numerical modeling and data assimilation
DE: 4263 Ocean prediction
DE: 3220 Nonlinear dynamics
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting