Model parameter estimation using coherent structure coloring

Lagrangian data assimilation is a complex problem in oceanic and atmospheric modeling. Tracking drifters in large-scale geophysical flows involves uncertainty in drifter location, complex inertial effects, and other factors which make comparing them to simulated Lagrangian trajectories from numerical models extremely challenging. Additionally, chaotic advection inherent in these flows tends to separate closely-spaced tracer particles, making error metrics based on drifter displacements unsuitable for estimating model parameters. We propose using error in the coherent structure coloring (CSC) field, a spatial representation of the underlying coherent patterns in a flow, to assess model skill. We show that error in the CSC field can be used to accurately determine multiple unknown model parameters simultaneously whereas an error metric based on error in drifter displacement fails. The effectiveness of this method suggests that Lagrangian data assimilation for multi-parameter oceanic and atmospheric models would benefit from a similar approach.