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Open AccessArticle

Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts

1
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA
2
Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
*
Author to whom correspondence should be addressed.
Fluids 2020, 5(4), 184; https://doi.org/10.3390/fluids5040184
Received: 21 August 2020 / Revised: 9 October 2020 / Accepted: 11 October 2020 / Published: 17 October 2020
(This article belongs to the Special Issue Lagrangian Transport in Geophysical Fluid Flows)
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. View Full-Text
Keywords: Lagrangian transport; spectral clustering; uncertainty quantification; parameter sensitivity; ocean ensemble forecast; drifter data; search-and-rescue Lagrangian transport; spectral clustering; uncertainty quantification; parameter sensitivity; ocean ensemble forecast; drifter data; search-and-rescue
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Vieira, G.S.; Rypina, I.I.; Allshouse, M.R. Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts. Fluids 2020, 5, 184.

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