Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles

Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles teaser image

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis. The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures. Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.

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Citation

Thomas Höllt, Ahmed Magby, Peng Zhan, Guoning Chen, Ganesh Gopalakrishnan, Ibrahim Hoteit, Charles D. Hansen, and Markus Hadwiger. Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles. IEEE Transactions on Visualization and Computer Graphics, 20(8): pp. 1114–1126, 2014.

BibTeX

@article{ bib:2014_tvcg,
author = {Thomas H{\"o}llt and Ahmed Magby and Peng Zhan and Guoning Chen and Ganesh Gopalakrishnan and Ibrahim Hoteit and Charles D. Hansen and Markus Hadwiger},
title = { Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles },
journal = { IEEE Transactions on Visualization and Computer Graphics },
volume = { 20 },
number = { 8 },
pages = { 1114 -- 1126 },
year = { 2014 },
doi = { 10.1109/TVCG.2014.2307892 },
}