Adaptive extraction and quantification of geophysical vortices.
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.
Institute for Data Analysis and Visualization at University of California, Davis, USA. sjwill@ucdavis.edu
Sean
S
Williams
Mark
M
Petersen
Peer-Timo
PT
Bremer
Matthew
M
Hecht
Valerio
V
Pascucci
James
J
Ahrens
Mario
M
Hlawitschka
Bernd
B
Hamann