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Review of Coastal Modeling Using HPC and ADCIRC+SWAN to Predict Storm Surge, Waves and Winds, Produced by a Hurricane

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High Performance Computer Applications (ISUM 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 595))

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Abstract

Scientists, engineers, universities, companies, and government agencies around the world, all of them related to coastal engineering issues, understand the importance that High Performance Computing (HPC) has brought to the way they approach larger and physically complex problems within this field. This technology is being used to predict the effects of large storms approaching the coast (near real time) and with this information help authorities to handle emergencies generated by a hurricane. This work will make a brief review, in its introduction, of the initiatives that have been developed in the Netherlands and USA by different research groups and universities to implement algorithms for storm-surge prediction. In Sect. 2 we will focus our attention on the implementation and progress made by a research group involving actors at government level, and universities to make ADCIRC a tool employed for the end mentioned above. In Sect. 3 entitled, ADCIRC+SWAN for the Mexican Pacific, we shall show how this tool is being used to hindcast storm-surge by expounding a newborn project that is being developed at UAEM to implement a similar approach for the Mexican Pacific. Finally in Sect. 4, Conclusions, we will indicate how the work developed so far has changed the perspective that we and other countries have for dealing with the effects of a hurricane impacting our coasts.

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Correspondence to Oscar Cruz-Castro .

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Cruz-Castro, O. (2016). Review of Coastal Modeling Using HPC and ADCIRC+SWAN to Predict Storm Surge, Waves and Winds, Produced by a Hurricane. In: Gitler, I., Klapp, J. (eds) High Performance Computer Applications. ISUM 2015. Communications in Computer and Information Science, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-32243-8_37

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  • DOI: https://doi.org/10.1007/978-3-319-32243-8_37

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