SWAN release version 41.20 included a new “package” of wave physics (referred to as ST6 physics). This package has new parameterizations of wind input, whitecapping, swell dissipation, wind speed scaling, and other processes (Rogers et al. 2012). The ST6 physics have been adopted by other wave models (e.g. NOAA’s WaveWatch III, Liu et al. 2019), and it may become the preferred physics package for SWAN. However, because the ST6 physics package has changes to so many parameterizations, it is necessary to quantify its effects on wave predictions. Recent studies (e.g. Aydogan and Ayat 2021) have demonstrated the benefits of using the ST6 physics in the standalone version of SWAN, but its effects have not been quantified for the coupled ADCIRC+SWAN (Dietrich et al. 2011a), which is used for real-time forecasts during impending storms. Do the ST6 physics improve the ADCIRC+SWAN wave predictions?
These analyses were performed by Carter Day, an undergraduate researcher in our team.
Like other spectral wave models, SWAN uses parameterizations to represent sources (e.g. wind), sinks (e.g. whitecapping, bottom friction, depth-limited breaking), and resonance (e.g. quadruplet and triad wave-wave interactions). Each parameterization is based on laboratory and experimental data to represent the transfer of energy to, from, and between waves. It is necessary for each term to represent its physical process, but it is also necessary for the terms to be calibrated collectively to represent their combined effects on wave evolution.
SWAN release version 41.31 was modified in two main ways: derivative computation was changed to use the Green-Gauss formula, and a new ‘package’ of wave physics (the so-called ST6 physics) was introduced. This package includes new parameterizations of wind input, whitecapping, swell dissipation, wind speed scaling, and other processes. The ST6 physics have been adopted by other wave models (e.g. NOAA’s WaveWatch III), and it will likely become the preferred physics package for SWAN. However, because the ST6 physics package has changes to so many parameterizations, it is necessary to quantify its effects on wave predictions during recent storms.
In this study, we simulate two recent hurricanes, Gustav (2008) and Florence (2018), and we compare wave predictions with the new ST6 physics package. Do the ST6 physics improve the SWAN wave predictions?
OM Nofal, JW van de Lindt, TQ Do, G Yan, S Hamideh, DT Cox, JC Dietrich (2021). “Methodology for Regional Multihazard Hurricane Damage and Risk Assessment.” Journal of Structural Engineering, 147(11), 04021185, DOI: 10.1061/(ASCE)ST.1943-541X.0003144.
S Crisanti, JC Dietrich. “Scalability of Widely Used Coastal Flooding Model.” Undergraduate Research Symposium, North Carolina State University, 29 July 2021.
OM Nofal, JW van de Lindt, G Yan, S Hamideh, JC Dietrich (2021). “Multi-Hazard Hurricane Vulnerability Model to Enable Resilience-Informed Decision.” Proceedings of International Structural Engineering and Construction, S El-Baradei, A Abodonya, A Singh, S Yazdani (eds.), 8(1), DOI: 10.14455/ISEC.2021.8(1).RAD-01.
CC Massarra, CJ Friedland, BD Marx, JC Dietrich (2020). “Multihazard Hurricane Fragility Model for Wood Structure Homes Considering Hazard Parameters and Building Attributes Interaction.” Frontiers in Built Environment, 6, 147, DOI: 10.3389/fbuil.2020.00147.
Then, to best utilize this new mesh, a multi-resolution approach is implemented to use meshes of varying resolution when and where it is required. It is hypothesized that by `switching’ from coarse- to fine-resolution meshes, with the resolution in the fine mesh concentrated only at specific coastal regions influenced by the storm at that point in time, both accuracy and computational gains can be achieved. As the storm approaches the coastline and the landfall location becomes more certain, the simulation will switch to a fine-resolution mesh that describes the coastal features in that region. Application of the approach during Hurricanes Matthew and Florence revealed the predictions to improve in both accuracy and efficiency, as compared to that from single simulations on coarse- and fine-resolution meshes, respectively.
Finally, the efficiency of the approach is further improved in the case of Hurricane Matthew, by using multiple smaller fine-resolution meshes instead of a single high-resolution mesh for the entire U.S. southeast coast. Simulations are performed utilizing predicted values of water levels, wind speeds, and wave heights, as triggers to switch from one mesh to another. Results indicate how to achieve an optimum balance between accuracy and efficiency, by using the above-mentioned triggers, and through a careful selection of the combination meshes to be used in the approach. This research has the potential to improve the storm surge forecasting process. These gains in efficiency are directly a savings in wall-clock time, which can translate into more time to invest in better models and/or more time for the stakeholders to consider the forecast guidance.