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.

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.

News: Improving Coastal Flooding Predictions

2018/05/14 – NC Sea Grant Coastwatch Currents
Hurricane Hindsight: Researchers Work to Improve Coastal Flooding Predictions

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Computer models can make surge predictions based on limited information about storm characteristics such as track, size, maximum wind speed and central pressure. Those parameters are used to predict the surface pressures and wind speeds throughout a coastal region. Those atmospheric conditions are then used to predict how the ocean will respond by generating large waves and surge, and by flooding into low-lying areas.

Given all the variables involved, there’s a lot of room for error in storm wind and surge prediction modeling.

For our study, we wanted to know how forecasting errors affect subsequent coastal flooding predictions. To that end, we needed to answer a couple of questions: First, as a storm moves closer to the coast, how accurate are forecasts of certain storm parameters like track, size, and maximum wind speed? Second, how do those forecasts affect predictions of wind speeds and storm surge?

Conference: ADCIRC 2018

R Cyriac, JC Dietrich, A Fathi, CN Dawson, KM Dresback, CA Blain, M Bilskie, SC Hagen, H Graber. “Wind Effects on the Choctawhatchee River Plume at Destin Inlet, Florida.” ADCIRC Users Group Meeting, NOAA Center for Weather and Climate Prediction, College Park, Maryland, 13 April 2018.

A Thomas, JC Dietrich, JG Fleming, BO Blanton, T Asher, RA Luettich. “High-Resolution Modeling of Surge during Hurricane Matthew.” ADCIRC Users Group Meeting, NOAA Center for Weather and Climate Prediction, College Park, Maryland, 13 April 2018.

N Tull, JC Dietrich, TE Langan, H Mitasova, BO Blanton, JG Fleming, RA Luettich. “Improving Accuracy of Real-Time Storm Surge Inundation Predictions.” ADCIRC Users Group Meeting, NOAA Center for Weather and Climate Prediction, College Park, Maryland, 13 April 2018.

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