Wave Resource Characterization Using an Unstructured Grid Modeling Approach

Abstract

Notably, spectral analysis indicates that the ST4 physics package improves upon the ST2 physics package's ability to predict wave power density for large waves, which is important for wave resource assessment, load calculation of devices, and risk management. In addition, bivariate distributions show that the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than with the ST2 physics package. This study demonstrated that the unstructured grid wave modeling approach, driven by regional nested grid WWIII outputs along with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (10 2 km).

A Sensitivity Analysis of the Wind Forcing Effect on the Accuracy of Large-Wave Hindcasting

“…Five sensitivity runs (Table 2) were conducted, including the baseline-condition simulations for the WWIII and UnSWAN models (Runs 1 and 2, respectively), in which both models were forced by the CFSR wind. The configuration of the baseline condition was also consistent with that in the previous studies [6,7]. Because the primary focus of this study was to evaluate whether better wind forcing, especially with the most accurate observational wind data at the buoys, can improve wave results, a sensitivity run (Run 4) with observed wind forcing was conducted for all UnSWAN domains.…”

“…A global wave model using the Climate Forecast System Reanalysis (CFSR) global wind product for the long-term wave hindcast also produced the largest errors during winter months and large-wave [5]. In our earlier studies [6,7], we successfully applied two third-generation spectral wave models, WaveWatch III (WWIII) [8,9] and the Unstructured version of Simulate Wave Near Shore (UnSWAN) [10], to simulate wave climates on the U.S. West Coast based on the National Oceanic and Atmospheric Administration's (NOAA's) National Centers for Environmental Protection (NCEP) global CFSR wind product [11]. Overall, the model-data comparisons showed satisfactory model performance with correlation coefficients (R) greater than 0.9 for both the omnidirectional power and significant wave height at nearly all validation National Data Buoy Center (NDBC) buoys.…”

“…Overall, the model-data comparisons showed satisfactory model performance with correlation coefficients (R) greater than 0.9 for both the omnidirectional power and significant wave height at nearly all validation National Data Buoy Center (NDBC) buoys. However, the results also indicated that, especially for the nearshore buoys, both models tend to underpredict the significant wave height and wave power during large-wave events [6,7].…”

“…Following the path of previous wave modeling work [6,7], we employed a similar multi-level nested-grid modeling approach in this study. This approach combines the strength of the WWIII and UnSWAN models in simulating waves in open oceans with structured grids and those in nearshore regions with flexible, unstructured grids.…”

“…The unstructured version of SWAN is especially suitable for simulating waves in nearshore regions with complex geometry. Specifically, we generated fine-resolution (from~100 m to several kilometers) UnSWAN model grids around the four NDBC buoys ( The model configuration for UnSWAN simulations was specified in the same way as that specified by Wu et al [7]; i.e., the model uses 24 direction bins and 29 spectral frequency bins with a logarithmic increment factor of 1.1 covering the frequency range from 0.035 Hz to 0.505 Hz. This spectral resolution meets the minimum requirements specified by the IEC TS; i.e., a minimum of 25 frequency components and 24 to 48 directional components, and a frequency range covering at least 0.04 to 0.5 Hz.…”