2011
Williams, Sean; Petersen, Mark; Bremer, Peer-Timo; Hecht, Matthew; Pascucci, Valerio; Ahrens, James; Hlawitschka, Mario; Hamann, Bernd
Adaptive extraction and quantification of geophysical vortices Journal Article
In: Visualization and Computer Graphics, IEEE Transactions on, vol. 17, no. 12, pp. 2088–2095, 2011, (LA-UR-11-04444).
Abstract | Links | BibTeX | Tags: adaptive extraction, feature extraction, Geophysical Vortices, Quantification, statistical data analysis, Vortex extraction
@article{williams2011adaptive,
title = {Adaptive extraction and quantification of geophysical vortices},
author = {Sean Williams and Mark Petersen and Peer-Timo Bremer and Matthew Hecht and Valerio Pascucci and James Ahrens and Mario Hlawitschka and Bernd Hamann},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AdaptiveExtractionAndQuantificaitonOfGeophysicalVortices.pdf},
year = {2011},
date = {2011-01-01},
journal = {Visualization and Computer Graphics, IEEE Transactions on},
volume = {17},
number = {12},
pages = {2088--2095},
publisher = {IEEE},
abstract = {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 geome try 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 dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.},
note = {LA-UR-11-04444},
keywords = {adaptive extraction, feature extraction, Geophysical Vortices, Quantification, statistical data analysis, Vortex extraction},
pubstate = {published},
tppubtype = {article}
}
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 geome try 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 dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.
: . .
1.
Williams, Sean; Petersen, Mark; Bremer, Peer-Timo; Hecht, Matthew; Pascucci, Valerio; Ahrens, James; Hlawitschka, Mario; Hamann, Bernd
Adaptive extraction and quantification of geophysical vortices Journal Article
In: Visualization and Computer Graphics, IEEE Transactions on, vol. 17, no. 12, pp. 2088–2095, 2011, (LA-UR-11-04444).
@article{williams2011adaptive,
title = {Adaptive extraction and quantification of geophysical vortices},
author = {Sean Williams and Mark Petersen and Peer-Timo Bremer and Matthew Hecht and Valerio Pascucci and James Ahrens and Mario Hlawitschka and Bernd Hamann},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AdaptiveExtractionAndQuantificaitonOfGeophysicalVortices.pdf},
year = {2011},
date = {2011-01-01},
journal = {Visualization and Computer Graphics, IEEE Transactions on},
volume = {17},
number = {12},
pages = {2088--2095},
publisher = {IEEE},
abstract = {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 geome try 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 dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.},
note = {LA-UR-11-04444},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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 geome try 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 dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.