Welcome to the CCHT! We develop computational models for wind waves and coastal circulation, and then apply these models to high-resolution simulations of ocean behavior. Our goals are to understand how coastlines are threatened during storms, how materials are transported in the coastal environment, and how to convey these hazard risks for use in decision support. Our research spans the disciplines of coastal engineering, numerical methods, computational mathematics, and high-performance computing.

In this web site, we share our research progress, from development to application, and from coding to publishing. Learn more about What We Do and how to Join Our Team.

Variability in Coastal Flooding Predictions due to Forecast Errors during Hurricane Arthur

Storm surge prediction models rely on an accurate representation of the wind conditions. In this paper, we examine the sensitivity of surge predictions to forecast uncertainties in the track and strength of a storm (storm strength is quantified by the power dissipation of the associated wind field). This analysis is performed using Hurricane Arthur (2014), a Category 2 hurricane, which made landfall along the North Carolina (NC) coast in early July 2014. Hindcast simulations of a coupled hydrodynamic-wave model are performed on a large unstructured mesh to analyze the surge impact of Arthur along the NC coastline. The effects of Arthur are best represented by a post-storm data assimilated wind product with parametric vortex winds providing a close approximation. Surge predictions driven by forecast advisories issued by the National Hurricane Center (NHC) during Arthur are analyzed. The storm track predictions from the NHC improve over time. However, successive advisories predict an unrealistic increase in the storm’s strength. Due to these forecast errors, the global root mean square errors of the predicted wind speeds and water levels increase as the storm approaches landfall. The relative impacts of the track and strength errors on the surge predictions are assessed by replacing forecast storm parameters with the best known post-storm information about Arthur. In a “constant track” analysis, Arthur’s post storm determined track is used in place of the track predictions of the different advisories but each advisory retains its size and intensity predictions. In a “constant storm strength” analysis, forecast wind and pressure parameters are replaced by corresponding parameters extracted from the post storm analysis while each advisory retains its forecast storm track. We observe a strong correlation between the forecast errors and the wind speed predictions. However, the correlation between these errors and the forecast water levels is weak signifying a non-linear response of the shallow coastal waters to meteorological forcing.

R Cyriac, JC Dietrich, JG Fleming, BO Blanton, C Kaiser, CN Dawson, RA Luettich (2018). “Variability in Coastal Flooding Predictions due to Forecast Errors during Hurricane Arthur.Coastal Engineering, 137(1), 59-78. DOI: 10.1061/(ASCE)WW.1943-5460.0000419.

Ayse Karanci wins Abstract Competition at iEMSs Conference

Ayse Karanci was a winner in the abstract competition at the 9th International Congress on Environmental Modelling and Software. The award included funding to support her travel to the conference in Fort Collins, Colorado, where she presented on “Cyberinfrastructure for Enhancing Interdisciplinary Engagement in Coastal Risk Management Research.”

Although Ayse was never an official member of the CCHT, she did contribute to our Risk Analytics Discovery Environment (RADE) project. Her presentation was related to that project, in which she developed containers for her models for coastal erosion and decision-making in coastal households. We are very proud of her good work.

Presentation: NSF Workshop 2018

News: Faster Storm Surge Forecasting

2018/06/12 – DHS Coastal Resilience Center of Excellence
NC State project aims to create faster storm surge forecasting

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Planning for a hurricane is a complicated process involving many stakeholders and varying degrees of uncertainty. Accurate predictions of storm surge and wave heights are vital to decision-making before, during and after the storm. Creating these predictions through modeling software can be expensive and time-consuming. When dealing with hurricanes, time is critical for emergency managers and other officials.

Helping decision-makers to save valuable prediction time is CRC Principal Investigator Dr. Casey Dietrich of North Carolina State University (NCSU). His project, “Improving the Efficiency of Wave and Surge Models via Adaptive Mesh Resolution,” involves collaboration with co-PI Dr. Clint Dawson at the University of Texas at Austin. Their project focuses on speeding up a widely used prediction tool, ADCIRC. His work with North Carolina Emergency Management during Hurricane Matthew in 2016, and his contributions to developing future disaster resilience specialists, have helped make significant contributions to disaster preparation and recovery.

Johnathan Woodruff

Updated 2018/06/07

 
Ph.D. Student (Graduate Research Assistant)
Department of Civil, Construction and Environmental Engineering
North Carolina State University
Mann Hall, Room 424
2501 Stinson Drive, Raleigh, NC 27607
jlwoodr3@ncsu.edu

 
 

Ahoy! I am a first year Ph.D. student in the Coastal and Computational Hydraulics Team (CCHT) at NC State. Having been born and raised in Florida, I developed a love for the coastline and a passion for understanding and protecting it. During my undergraduate studies at the University of Florida, I took a few classes in coastal/water resources engineering and decided to pursue it further with a master’s degree at Georgia Tech. There I specialized in coastal and water resources engineering and found my passion.

At Georgia Tech, I took a particular interest in Coastal Hazards work which led me to the CCHT here at NC State. I am currently working on the NSF project “Subgrid-Scale Corrections to Increase the Accuracy and Efficiency of Storm Surge Models,” which aims to reduce computation times of storm surge forecasting while retaining the same level of accuracy used in high resolution models. Although I am just starting out, I am extremely excited to dive deeper into this project so that I may better understand the complex numerical processes that are involved in storm surge prediction. I hope to incorporate rapid deployment field observations into my Ph.D. to help validate the results of our models. In addition, I would like to investigate the interaction between storm surge and rainfall events and its affect on both coastal and inland structures.

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