Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations

Abstract

Abstract. The performance of two methods for quantifying whitecapping dissipation incorporated in the Simulating Waves Nearshore (SWAN) wave model is evaluated for waves generated along and off the US east coast under energetic winter storms with a predominantly westerly wind. Parameterizing the whitecapping effect can be done using the Komen-type schemes, which are based on mean spectral parameters, or the saturation-based (SB) approach of van der Westhuysen (2007), which is based on local wave parameters and the saturation level concept of the wave spectrum (we use “Komen” and “Westhuysen” to denote these two approaches). Observations of wave parameters and frequency spectra at four National Data Buoy Center (NDBC) buoys are used to evaluate simulation results. Model–data comparisons show that when using the default parameters in SWAN, both Komen and Westhuysen methods underestimate wave height. Simulations of mean wave period using the Komen method agree with observations, but those using the Westhuysen method are substantially lower. Examination of source terms shows that the Westhuysen method underestimates the total energy transferred into the wave action equations, especially in the lower frequency bands that contain higher spectral energy. Several causes for this underestimation are identified. The primary reason is the difference between the wave growth conditions along the east coast during winter storms and the conditions used for the original whitecapping formula calibration. In addition, some deficiencies in simulation results are caused along the coast by the “slanting fetch” effect that adds low-frequency components to the 2-D wave spectra. These components cannot be simulated partly or entirely by available source terms (wind input, whitecapping, and quadruplet) in models and their interaction. Further, the effect of boundary layer instability that is not considered in the Komen and Westhuysen whitecapping wind input formulas may cause additional underestimation.

Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

“…For all stations, these comparisons show that the model-derived contours capture expected trends that affect the contour size as measured by the range of ( ) and ( ) . As expected, the maximum values of ( ) increase with return period and with latitude along both coasts, as more energetic wave climates are found in northern latitudes [21,29]. There is no similar north-south trend in maximum values of ( ) .…”

“…Depths at these stations vary from 30 m (44009) to 4048 m (41002) and are classified as intermediate and deep wave sites with normalized peak frequencies of f P > 0.05 g/h. resolution of 200 m for coastal areas extending 20 km offshore and a resolution of 1-3 km for the inner-shelf regions [21,29]. At thirty years or more, these model hindcasts exceed minimum requirements for estimating extreme wave heights with return periods up to fifty years.…”

“…The present study utilizes modelled hindcast data from several wave model studies to estimate extreme wave statistics: a WWIII 30-year model (v.5.08) hindcast study encompassing all US coastal waters with a spatial resolution of 4 minutes (6-7 km) [28], a high-resolution regional SWAN 32-year model hindcast study for the US West Coast with a spatial resolution of 200-300 m [20], and a high-resolution regional SWAN 32-year model hindcast study for the US East Coast with a spatial resolution of 200 m for coastal areas extending 20 km offshore and a resolution of 1-3 km for the inner-shelf regions [21,29]. At thirty years or more, these model hindcasts exceed minimum requirements for estimating extreme wave heights with return periods up to fifty years.…”

Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

“…Thus, it should be noted that although both databases are built based on previous experience in processing satellite measurements or simulating wave conditions, they have been available for research only for a very short time, and therefore there are few studies on data quality. Although the wave simulations for ERA5 were performed with updated bathymetry and using an improved wave model, errors may still be present due to the coarse resolution of the computational grid (especially in coastal areas or enclosed basins); these can be also due to errors in the wind field that was used to force the wave model or some model parameterizations considered [65]. Moreover, some errors can be present in the altimeter measurements, especially in the coastal areas.…”

Joint Modelling of Wave Energy Flux and Wave Direction

“…The local wave resource assessment can be based on buoy measurements, reanalysis results, and satellite altimetry data commonly through spectral analysis. See also [9][10][11][12][13][14]. Such studies are usually confined in the statistical description of particular wave spectral parameters, such as significant wave height defined as ≅ 4√ 0 ; energy period , , = ⋯ , −2, −1,0,1, ⋯ , is the − order spectral moment, ( ) is the spectral density function, and is the spectral frequency; see, e.g., [15].…”