Computational Approaches to Improving Storm Surge Forecasting

Monday, 15 December 2014
Kyle T Mandli, Columbia University, Applied Physics and Applied Mathematics, New York, NY, United States
Coastal hazards related to strong storms are one of the most frequently recurring and wide spread hazards to coastal communities today. In particular storm surge, the rise of the sea surface in response to wind and pressure forcing from these storms, can have a devastating effect on the coastline. Therefore, the ability to predict these events quickly and accurately is critical to the protection and sustainability of these coastal areas.

Computational approaches to the forecasting of storm surge must be able to represent the inherent multi-scale nature of the surge while remaining computationally tractable and physically relevant. This has commonly been accomplished by solving a depth-averaged set of fluid equations on a non-uniform, unstructured grid. These approaches, however, have often had shortcomings due to computational expense, the need for involved model tuning, and missing physics.

In this talk, I will outline some of the approaches being developed to address several of these shortcomings through the use of advanced computational approaches including adaptive mesh refinement, higher levels of parallelism including many-core technologies, and more accurate model equations such as the multilayer shallow water equations. Combining these approaches promises to address some of the pressing issues with current state-of-the-art models while continuing to decrease the computational overhead needed to calculate a forecast.