Observing System Design for Lagrangian Data Assimilation: Targeting Lagrangian Coherent Structures

Abstract: The use of Lagrangian floats/ drifters in the ocean has become more widespread due to their ability to track coherent flow features (e.g.\ meddies) and their relevance in understanding mesoscale transport. More recently, it has become apparent that such data can be valuable in the forecasting of the ocean. To realize this goal, we have developed a method for assimilating Lagrangian data into a shallow-water equation ocean model. The method employs an augmented state vector that includes Lagrangian drifter coordinates and uses an Ensemble Kalman Filter to evolve the error correlations between the drifters and the flow in our model. We have shown that the method can track the `true' system in our twin experiment configuration provided the assimilation time interval is of the order of the Lagrangian integral time-scale. The success of the method is intrinsically tied to the launch sites chosen for the drifters. Given this, a directed launch strategy is needed for the methodology to be useful in operational forecasts. In this work, we employ a dynamical systems approach to extract Lagrangian coherent structures of the flow. We show that by targeting the Lagrangian centers residing within the energy dominated eddies, and the Lagrangian hyperbolic saddle points, an optimum drifter deployment is realized for the performance of the method.