Visualizing uncertainties in a storm surge ensemble data assimilation and forecasting system
KAUST DepartmentComputer Science Program
Water Desalination and Reuse Research Center (WDRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Physical Sciences and Engineering (PSE) Division
Environmental Science and Engineering Program
Earth Fluid Modeling and Prediction Group
Permanent link to this recordhttp://hdl.handle.net/10754/575634
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AbstractWe present a novel integrated visualization system that enables the interactive visual analysis of ensemble simulations and estimates of the sea surface height and other model variables that are used for storm surge prediction. Coastal inundation, caused by hurricanes and tropical storms, poses large risks for today's societies. High-fidelity numerical models of water levels driven by hurricane-force winds are required to predict these events, posing a challenging computational problem, and even though computational models continue to improve, uncertainties in storm surge forecasts are inevitable. Today, this uncertainty is often exposed to the user by running the simulation many times with different parameters or inputs following a Monte-Carlo framework in which uncertainties are represented as stochastic quantities. This results in multidimensional, multivariate and multivalued data, so-called ensemble data. While the resulting datasets are very comprehensive, they are also huge in size and thus hard to visualize and interpret. In this paper, we tackle this problem by means of an interactive and integrated visual analysis system. By harnessing the power of modern graphics processing units for visualization as well as computation, our system allows the user to browse through the simulation ensembles in real time, view specific parameter settings or simulation models and move between different spatial and temporal regions without delay. In addition, our system provides advanced visualizations to highlight the uncertainty or show the complete distribution of the simulations at user-defined positions over the complete time series of the prediction. We highlight the benefits of our system by presenting its application in a real-world scenario using a simulation of Hurricane Ike.
SponsorsWe would like to thank the anonymous reviewers for the constructive comments. Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).