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Issue No.12 - Dec. (2011 vol.17)
pp: 2088-2095
S. Williams , Inst. for Data Anal. & Visualization, Univ. of California, Davis, CA, USA
M. Petersen , Los Alamos Nat. Lab., Los Alamos, NM, USA
P.-T Bremer , Lawrence-Livermore Nat. Lab., Livermore, CA, USA
M. Hecht , Los Alamos Nat. Lab., Los Alamos, NM, USA
V. Pascucci , Sci. Comput. & Imaging Inst., Univ. of Utah, Salt Lake City, UT, USA
J. Ahrens , Los Alamos Nat. Lab., Los Alamos, NM, USA
M. Hlawitschka , Univ. Leipzig, Leipzig, Germany
B. Hamann , Inst. for Data Anal. & Visualization, Univ. of California, Davis, CA, USA
We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geometry of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real data from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.
Feature extraction, Information analysis, Data visualization, Data mining, Data models, Atmospheric modeling, statistical data analysis., Vortex extraction, feature extraction
S. Williams, M. Petersen, P.-T Bremer, M. Hecht, V. Pascucci, J. Ahrens, M. Hlawitschka, B. Hamann, "Adaptive Extraction and Quantification of Geophysical Vortices", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 2088-2095, Dec. 2011, doi:10.1109/TVCG.2011.162
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