Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts
| Autor: | Irina I. Rypina, Guilherme S. Vieira, Michael Allshouse |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
uncertainty quantification Computer science FOS: Physical sciences Initialization lcsh:Thermodynamics 01 natural sciences 010305 fluids & plasmas lcsh:QC310.15-319 0103 physical sciences Lagrangian transport search-and-rescue Range (statistics) 14. Life underwater Uncertainty quantification Cluster analysis Physics::Atmospheric and Oceanic Physics lcsh:QC120-168.85 0105 earth and related environmental sciences Fluid Flow and Transfer Processes spectral clustering Forcing (recursion theory) Mechanical Engineering Fluid Dynamics (physics.flu-dyn) Physics - Fluid Dynamics Condensed Matter Physics Spectral clustering Physics - Atmospheric and Oceanic Physics Drifter 13. Climate action Atmospheric and Oceanic Physics (physics.ao-ph) parameter sensitivity drifter data lcsh:Descriptive and experimental mechanics ocean ensemble forecast Algorithm Geostrophic wind |
| Zdroj: | Fluids Volume 5 Issue 4 Fluids, Vol 5, Iss 184, p 184 (2020) |
| ISSN: | 2311-5521 |
| DOI: | 10.3390/fluids5040184 |
| Popis: | 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. 21 pages, 11 figures, Supplementary Material A and B at the end of the document |
| Databáze: | OpenAIRE |
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