Drifter deployment strategies to estimate surface dilation rate in submesoscale flows

Surface convergence in the ocean is associated with accumulation of buoyant pollutants as well as with subduction that is important to biological activity. Recent studies on submesoscale flows have shown that the finite-time Lagrangian average divergence (i.e., the dilation rate) is even better at identifying surface clustering regions and subduction conduits near density fronts than the instantaneous horizontal divergence field. Divergence can be derived from any velocity measurements, such as radar or ADCPs, but for the Lagrangian average, the most convenient methodology is based on the time rate of change of the area encompassed by drifter swarms. The technological advances that have enabled the deployment of large numbers of drifters in a single experiment have raised new questions about optimal deployment strategies for extracting dilation rate information with acceptable accuracy and as much spatial coverage as possible. Using a submesoscale-resolving operational model of the Mediterranean Sea, we analyze synthetic drifter trajectories to evaluate the impact the number of drifters and their initial separation have on the accuracy of the resulting dilation rate estimates. The results confirm that estimates improve as the swarm radius decreases and as more drifters are added, but with only a marginal improvement for swarms containing more than four drifters. GPS positions obtained from drifters in the ocean are subject to uncertainty on the order of 5-10 meters, and when this uncertainty is taken into account, an optimal initial separation distance can be identified that balances uncertainty from position measurements with that from the dilation rate estimation.  Finally, we investigate the effect of the initial configuration of drifter swarms using various shape parameters to classify the shapes of drifter triads and tetrads.