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We outline a new approach to objectively locate and define mesoscale oceanic features from satellite derived ocean color data. Modern edge detection algorithms are robust and accurate for most applications, oceanic satellite observations however introduce challenges that foil many differentiation based algorithms. The clouds, discontinuities, noise, and low variability of pertinent data prove confounding. In this work the input data is first quantized using a centroidal voronoi tesselation (CVT), removing noise and revealing the low variable fronts of interest. Clouds are then removed by assuming values of its surrounding neighbors, and the perimeters of these resulting cloudless regions localize the fronts to a small set. We then use the gradient of the quantized data as a compass to walk around the front and periodically select points to be knots for a Hermite spline. These Hermite splines yield an analytic representation of the fronts and provide practitioners with a convenient tool to calibrate their models.
A Thesis submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Master of Science.
Includes bibliographical references.
Gordon Erlebacher, Professor Co-Directing Thesis; Eric Chassignet, Professor Co-Directing Thesis; Ming Ye, Committee Member; Anke Meyer-Baese, Committee Member.
Florida State University
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