Lagrangian Data Assimilation in Ocean Models

Wednesday, May 1, 2002 - 9:00am - 9:45am
Keller 3-180
Christopher Jones (Brown University)
Ocean drifters and floats gather velocity field information along their trajectories. Difficulties arise in the assimilation of Lagrangian data because the state of the prognostic model is usually described in terms of Eulerian variables. There is no direct connection between the model variables and Lagrangian observations which carries time-integrated information. We present a method, based on the extended Kalman filter, for assimilating drifter/float positions, observed at discrete times, directly into the model.

The technique is tested on point vortex flows. Its performance is evaluated on ensembles associated with different noise realizations. It is also compared to an alternative indirect approach in which the flow velocity, estimated from two (or more) consecutive drifter observations, is assimilated. The influence of flow features, such as saddle points of the velocity field, on the performance of the scheme is analyzed.

This is joint work with Kayo Ide (UCLA) and Leonid Kuznetsov (Brown).