Scalable and automated workflow in mining large-scale severe-storm simulations

Authors:
Lei Jiang;Gabrielle Allen;Qin Chen
Affiliations:
Department of Computer Science, Louisiana State University and Center for Computation and Technology, Louisiana State University;Department of Computer Science, Louisiana State University and Center for Computation and Technology, Louisiana State University;Department of Civil and Environmental Engineering, Louisiana State University and Center for Computation and Technology, Louisiana State University
Venue:
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Year:
2011

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Abstract

The simulation of large-scale complex systems, such as modeling the effects of hurricanes or storms in coastal environments, typically requires a large amount of computing resources in addition to data storage capacity. To make an efficient prediction of the potential storm surge height for an incoming hurricane, surrogate models, which are computationally cheap and can reach a comparable level of accuracy with simulations, are desired. In this paper, we present a scalable and automated workflow for surrogate modeling with hurricane-related simulation data.