Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts

TL;DR

This paper presents a method for quantifying uncertainty in ocean trajectory clustering by analyzing ensemble forecasts, improving search-and-rescue planning through robust region identification despite forecast uncertainties.

Contribution

It introduces an ensemble-based spectral clustering approach that assesses the robustness of ocean flow partitions under forecast uncertainties, validated with real-world drifter data.

Findings

Accurately identifies low-uncertainty regions with stable drifter trajectories.

High-uncertainty regions show drifters deviating from predicted clusters.

Ensemble statistics reveal sensitivity of clustering to forecast parameters.

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 accurately identifies regions of low uncertainty where drifters released within a cluster 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.