AN EFFICIENT STORM SURGE FORECASTING TOOL FOR COASTAL MISSISSIPPI
Proceedings of the 32nd International Conference
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Keywords

storm surge forecasting
emergency management
hurricane
coastal Mississippi

How to Cite

Das, H. S., Jung, H., Ebersole, B., Wamsley, T., & Whalin, R. W. (2011). AN EFFICIENT STORM SURGE FORECASTING TOOL FOR COASTAL MISSISSIPPI. Coastal Engineering Proceedings, 1(32), currents.21. https://doi.org/10.9753/icce.v32.currents.21

Abstract

Coupled storm surge simulations with fine resolution have become a reality due to the rapid development of computer power and advancement in the integration of the simulation models. However, the real-time application of such robust simulations is often constrained by the availability of time and computational resources. In this study, an alternative, Storm Surge Forecasting Tool (SSFT) has been developed to forecast storm surge in Coastal Mississippi. The algorithm of SSFT uses a weight based Storm Similarity Index (SSI) that is defined by current hurricane position Central Pressure (CP), Pressure Scale Radius (Rmax) along with hurricane track, landfall location, storm forward speed, and forecasted storm track published by the National Hurricane Centre (NHC) and correlated with the characteristics of synthetic storms within the underlying database. Based on the values of SSI (scales from 0 to 1), the SSFT identifies a group of storms that much as close as possible with the characteristics of the approaching hurricanes and then display high resolution simulation results (e.g., maximum surge elevation and hydrographs). The SSFT model operates in two different modes:1) Hindcasting mode and 2) Forecasting mode. The SSFT GUI was tested in both modes and we found that the method is very promising. Using this tool and approach as a decision aide, the emergency personnel can quickly forecast local storm surge along the coast of Mississippi. This will allow them to make quantitative and objective decisions by evaluating "what-if-scenarios† starting two to three days ahead of the landfall.
https://doi.org/10.9753/icce.v32.currents.21
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