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.
Highlights
Fluid flows, even if unsteady and aperiodic, may admit persistent patterns generally referred to as coherent structures that reveal flow characteristics related to the transport of fluid particles [1,2,3].Coherent structures of the elliptic type [4,5,6] are portions of fluid that do not significantly mix with the rest of the domain
The mean membership probability at the core goes up to over 0.95 for K = 7, corresponding to a drop in the standard deviation. As another cluster is added for K = 8 and the jet is identified, Figure 6g,h reveals a modest drop in the mean values compared to K = 7, associated to an increase in the standard deviation for the vortex core from 0.08 to 0.12. While these ensemble statistics could be used as a basis for setting the method free-parameters, a thorough investigation on this is beyond the focus of the parameter sensitivity study presented in this paper
Ensemble statistics of the trajectory clustering results provide a partitioning of the fluid domain that may provide critical information in emergency response situations, such as search-and-rescue operations, when operational decisions about optimal resource allocation need to be made quickly, accurately, and account for model uncertainties
Summary
Even if unsteady and aperiodic, may admit persistent patterns generally referred to as coherent structures that reveal flow characteristics related to the transport of fluid particles [1,2,3]. We analyze particle trajectories using the spectral clustering algorithm [16,17,18,19] This method has been used to identify coherent structures in analytic and simulated flows [15,20,21]. We apply the spectral clustering algorithm varying the method free-parameters to understand how the clustering results are sensitive to the implementation, analyze ensemble simulations to understand how the model parameters impact the resulting clusters, and apply the method to a forecast data set to compare the clustering prediction to experimental drifter data.
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