Predicting Large Ocean Wave Events Characterized by Bimodal Energy Spectra in the Presence of a Low-Level Southerly Wind Feature

Abstract

Three large wave events are simulated with WaveWatch III using different wind inputs and physics packages. The modeled output, including spectral shape and bulk parameter time series, are compared with National Data Buoy Center buoy observations offshore of Newport, Oregon. The atmospheric conditions that generate these large waves include a strong southerly wind along with a distant cyclone. The energetic contributions of these simultaneously occurring atmospheric features result in a wave field characterized by bimodal energy spectra for two events and unimodal energy spectra for the third event. The analysis of model output evaluates bulk parameter time series of significant wave height, mean period, and mean wave direction derived from partitioned energy spectra. A consistent underestimation in wave energy approaching from the southwestern direction is found for the output associated with all model configurations. This wave energy is generated by the southerly wind. An overestimation in swell energy approaching from the northwest is also found for all model configurations. The model configuration that most accurately reproduces the southerly wave energy results in the best performance for the overall bulk parameters.

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

“…As pointed out by other similar studies, the underestimation of large waves could be partially constrained by the accuracy of wind forcing during the storm events, especially of those operational global wind-model products [2][3][4]12]. A quick comparison of the CFSR wind with observed wind at NDBC buoys also confirmed that the discrepancy in wave predictions appears to be consistent with that in the wind forcing.…”

“…For instance, Pan et al [3] evaluated the performance of an operational wind wave forecasting system in Taiwan, and found the averaged peak wave heights were underestimated during typhoon events. By using multiple wind inputs to model a cyclone event, a regional wave model used near Newport, Oregon underestimated the large waves for all simulations [4]. 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].…”

Integrated Observations of Global Surface Winds, Currents, and Waves: Requirements and Challenges for the Next Decade

“…Numerical models of surface waves show substantial along-coast variability in wave heights near shore due to refraction over canyons and other bathymetric features on continental shelves (García-Medina et al, 2013). Temporal and spatial variability in wave heights also occurs due to coastal boundary jets formed when mountains block passing fronts (Ellenson and Özkan-Haller, 2018). Fully coupled models for wave and current prediction are underway to aid safety and planning for marine shipping and navigation, especially near river mouths (Akan et al, 2017); simultaneous satellite measurements of winds, waves, and surface currents would enable testing and improving these models.…”

Kinematics and Statistics of Breaking Waves Observed Using SWIFT Buoys

“…WW3 was used to model the five storms during which the SWIFTs were deployed. The primary input to WW3 is the windfield, and the fidelity of the wave predictions are directly linked to the quality of the wind product [40]. Two different wind prod-ucts were used, both of which are provided by NCEP.…”

“…Although WW3 does not provide predictions collocated with the exact locations of the SWIFTs, wave spectra are extracted from the WW3 output by interpolating results from the model grid to the space and time locations that correspond to the SWIFT locations. This method is often applied for WW3 comparisons with stationary buoy data, and has also been successfully applied to comparisons with drifting buoys [40].…”