Observing system design for Lagrangian data assimilation: Dynamical systems perspective
We present the Lagrangian data assimilation (LaDA) method. We invoke an ensemble Kalman filter in order to estimate and forecast the (ocean) state using the shallow-water model. Based on the augmented state representation, the LaDA eliminates the need for any conventionally used approximation in assimilating the Lagrangian information. This augmentation also allows us to use dynamical systems theory for the design of a comprehensive observing system. We show how deploying drifters in the flow near the (Lagrangian) saddle point enhances the information content of the (Eulerian) flow dynamics extracted from the Lagrangian data using LaDA.