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
I am a first year PhD student in the CCHT at North Carolina State University. I am originally from Michigan where we have a different kind of coastline, but a very large one none the less. Living near the Great Lakes, my love for the natural environment was always present. I studied environmental science and mathematics at the University of Michigan for my undergraduate degree and then ecology for my masters. After graduating, I worked with various organizations on things related to environmental education, communication and outreach, and fresh water studies, and in a more pivotal position working on a coastal watershed restoration project on a small island in the Pacific Ocean. It was here where I fell in love with that other coast, the salty one. Afterward, I worked in landscape limnology (freshwater) research at Michigan State University with some truly amazing people who helped me develop an interest in the research process. From there I decided I needed a new challenge and needed to develop my own research interests, so I decided to apply to go back to school for my PhD.
During the start of my PhD, I will be working on the the NSF project “Subgrid-Scale Corrections to Increase the Accuracy and Efficiency of Storm Surge Models,” which has the goal of incorporating the more nuanced structure of coastal landscapes available in higher resolution storm surge models into broader scaled models thus reducing computation times while also maintaining model accuracy. Since I am just starting, my research interests have not been fully formed and are still rather broad. However, I hope to use my numerical modeling and quantitative skills to assist coastal communities as they prepare for the unforeseen changes they will experience. This includes learning more about coastal processes and coastal modeling to understand how storm events and subsequent storm surge will impact and alter the coastal landscape.
In addition to my work at NC State, I am also passionate about my personal health and fitness so I spend most of my free time either at the gym or cooking in the kitchen. I also love to read and spend time in the outdoors. I hope to experience all that North Carolina has to offer from the mountains to the sea.
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.
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.
NC State project aims to create faster storm surge forecasting
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.