CCEE Researchers respond rapidly to Hurricane Florence
Much of the North Carolina coast is lined with sandy beaches and dunes, which can erode during storms, allowing sand onto major roadways and floodwaters into communities. To develop predictions for this erosion and its effects on infrastructure, it was critical to collect observations shortly after the storm. A multi-disciplinary team led by Dr. Elizabeth Sciaudone traveled to Dare County to collect time-sensitive data at Kitty Hawk, Nags Head, Pea Island, and Hatteras Island. Working in conjunction with the Institute for Transportation Research and Education (ITRE), the Center for Geospatial Analytics in the College of Natural Resources, and industry partner SenseFly, researchers surveyed beach and dune changes. Real-Time Kinematic GPS equipment was used to survey select cross-shore beach and dune profiles and document the extent of dune erosion and overwash (inland sand deposits), such as when NC Highway 12 becomes covered after large storms.
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.