Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts

Abstract

Partitioning ocean flows into regions dynamically distinct from their surroundings based on material transport can assist search-and-rescue planning by reducing the search domain. The spectral clustering method partitions the domain by identifying fluid particle trajectories that are similar. The partitioning validity depends on the accuracy of the ocean forecasting, which is subject to several sources of uncertainty: model initialization, limited knowledge of the physical processes, boundary conditions, and forcing terms. Instead of a single model output, multiple realizations are produced spanning a range of potential outcomes, and trajectory clustering is used to identify robust features and quantify the uncertainty of the ensemble-averaged results. First, ensemble statistics are used to investigate the cluster sensitivity to the spectral clustering method free-parameters and the forecast parameters for the analytic Bickley jet, a geostrophic flow model. Then, we analyze an operational coastal ocean ensemble forecast and compare the clustering results to drifter trajectories south of Martha’s Vineyard. This approach identifies regions of low uncertainty where drifters released within a cluster predominantly remain there throughout the window of analysis. Drifters released in regions of high uncertainty tend to either enter neighboring clusters or deviate from all predicted outcomes.

A Relocatable Ocean Modeling Platform for Downscaling to Shelf-Coastal Areas to Support Disaster Risk Reduction

“…This could be due to limitations in our modeling hypothesis, such as the low spatial/temporal variability of wind forcing and the neglected effects of Stokes drift. However, the inherent limited predictability of objects floating at sea can only be ameliorated by ensemble methods (Vieira et al, 2020).…”

Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories

“…Again, this is certainly a topic that warrants further investigation. We also see potential for our work to naturally follow-on from the recent methodology developed in [25] who identify clusters of drifter trajectories that share coherent structures. For example, such clustering could be used to divide larger deployments into smaller clusters, after which our method can then be applied to each cluster to separate flow components within coherent structures.…”

“…We shall refer to this reduced technique as the second-moment fitting method. In contrast, we refer to the full estimation technique from Equations (18)- (25) as the first and second-moment fitting method.…”

“…The first method is perhaps the most intuitive. Here we calculate how much variance remains in the 'unexplained' residual submesoscale velocities found in Equation (25). This value in itself, however, is not a meaningful quantity unless it is presented in reference to some other quantity.…”

“…The background flow and submesoscale flow are still recovered using Equations (24) and (25), respectively.…”