1. Andrew Poje (1), Anne Molcard (2,3), Tamay Ö zg Ö kmen (4) 1 Dept of Mathematics, CSI-CUNY,USA 2 LSEET - Universite de Toulon et du Var, France 3 ISAC-CNR Torino, Italy 4 RSMAS/MPO, Univ. of MIAMI, USA Direct drifter launch strategies for Lagrangian data assimilation using hyperbolic trajectories

2. Problem Given a model assimilating Lagrangian data, determine initial launch locations which accelerate convergence of model to ‘truth’. Well defined optimization problem? Xo -> Nonlinear ODE’s -> +Assimilation + Nonlinear PDE -> Model Output High dimensionality Unique solutions? ‘Simple Case’ - perfect model How long? What domain?

3. Properties of “Good” Lagrangian Data Span velocity space Span physical space Independent measurements Provide continuous velocity corrections Capture phase and position of energetic structures in field Approach Molcard et al. Lagrangian Assimilation + Lagrangian C.S. Launch Strategy Target hyperbolic trajectories - maximize relative dispersion track coherent feature boundaries

4. The assimilation scheme (OI) U a =U b +K (Y o -H(U b )) K=BG T (GBG T +E) -1 U a ij =U b ij +  -1   ijm (V o m -V b m )  =1 +  o 2 /  b 2 Oth order reduction: assimilation frequency which resolve the gradients; Observed and simulated variables non correlated in space and time; Gaussian distribution of the correction; Only 1  t or 2 successive positions. Ua analysis variable Ub model calculated variable Yo observed variable H(Ub) observation matrix that transforms the forecast data in the observed variable at the observed point G=dH(Ub)/dUb sensitivity matrix B, E model and observation covariance matrices

5. A1 :Observation (t 0 ) A2: Observation (t 0 +  t) C1: First guess (t 0 +  t) C2: Corrected forecast (t 0 +  t) A1 C1 C2 A2

6. Assimilation results for double-gyre, 1.5 layer MICOM

7. inflow outflow A direct launch strategy, based on tracking the Lagrangian manifolds emanating from strong hyperbolic regions in the flow field is developed by computing the eigen-structure of the Eulerian fied at t i and t f and use it to initialize the manifold calculation. Optimal deployment strategy

8. Manifolds W s inflowing: attracted toward the hyperbolic trajectory in forward time W u outflowing: repelled away from hyperbolic point

9. FSLE Finite Scale Lyapunov Exponents (measure of time required for a pair of particle trajectories to separate)

10. Deployments: Each 3 lines of 4 drifters: 1 ‘Optimal’ (Directed) on Wu 50 Randomly Choice

11. Analysis: 50 random deployments vs 1 optimal in terms of: overall coverage (or spreading), integrated Kinetic energy, integrated distance between drifter observations and simulated trajectories. Spreading KE Y O -Y B Correction The product of the overall coverage and the integrated distance difference provides a good proxy for both terms in the correction term in the assimilation scheme  ijm (V o m -V b m )

12. E=100%E=60%E=40% Control Assimilation Free DAY 0DAY 30DAY 0DAY 30

14. Dependance on the length of the segment and on the “exact point” error

16. Conclusion Alignment of initial drifter positions along the out-flowing branch of identifiable Lagrangian boundaries is shown to optimize both the relative dispersion of the drifters and the sampling of high kinetic energy features in the flow. The convergence of the assimilation scheme is consistent and considerably improved by this optimal launch strategy. ‘Real’ ocean? Identifiable structures Appropriate time/space scales?(Dart05?) Something slightly deeper here? Fast convergence to attractor (Wu) Knowing location of attractor => more information Questions Preprint available: Ocean Modeling, 2006