Addressing Needs in Observed and Simulated Storm Surge Data for Uncertainty Quantification

Taylor Asher, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, Rick Luettich, University of North Carolina at Chapel Hill, Institute of Marine Sciences, Morehead City, United States, Jennifer L Irish, Virginia Polytechnic Institute and State University, Department of Civil and Environmental Engineering, Center for Coastal Studies, Blacksburg, United States, Pulong Ma, Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC, United States, Michelle Bensi, University of Maryland College Park, Department of Civil and Environmental Engineering, College Park, MD, United States and Donald Resio, University of North Florida, Jacksonville, FL, United States
Abstract:
Limits in the quantity of observed and simulated storm surge data have historically hampered rigorous investigations of model uncertainty and sensitivity, which are essential to improving understanding of both the real and modeled systems. However, through a combination of above-average hurricane frequency, new measurement techniques, and major flood studies, there has been substantial growth in available historical data. We have compiled a large dataset of hindcast studies from several groups with dozens of storms and thousands of both measured and modeled water levels. We’ve also produced a large database of tens of thousands of synthetic storm simulations across a region of varied terrain with several model configurations. We are now leveraging these observed and simulated data of both real and synthetic storms for a host of analyses including model uncertainty and sensitivity, machine learning, and flood hazard sensitivity. We are also making these data available to other researchers to act as both an educational tool and a basis for further research. This presentation will provide an overview of the data and work to-date, focusing on key results and steps forward.
Back to: Extreme Sea Levels and Coastal Flood Risk I