Lagrangian analysis by clustering

We propose a new method for obtaining average velocities and eddy diffusivities from Lagrangian data. Rather than grouping the drifter-derived velocities in geographical bins, we group them by nearest-neighbor distance using a clustering algorithm. This yields sets with approximately the same number of observations, covering unequal areas. A major advantage is that, because the number of observations is the same for the clusters, the statistical accuracy is more uniform than with geographical bins. We illustrate the technique using synthetic data from a stochastic model, employing a realistic mean flow. The latter represents the surface currents in the Nordic Seas and is strongly inhomogeneous in space. We use the clustering algorithm to extract the mean velocities and diffusivities and compare the results with the corresponding quantities from the stochastic model. We perform a similar comparison with the means and diffusivities obtained with geographical bins. Clustering is more successful at capturing the mean flow and improves convergence in the eddy diffusivity estimates. We discuss both the advantages and shortcomings of the new method