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In this study, a numerical simulation is performed to produce wave hindcasting data from 2007 to 2018 for the assessment of wave energy resources in the sea waters of Korea. The hindcasting data are obtained with a relatively fine spatial resolution of 1/20° covering 120–150 °E longitude and 22.4–47.6 °N latitude using the Simulating WAves Nearshore wave model (SWAN). Three different wind fields, those of the European Centre for Medium-Range Weather (ECMWF), National Centers for Environmental Prediction (NCEP), and Japan Meteorological Agency (JMA), are used for the numerical wave simulation. It is observed that the wind field dataset of JMA exhibits the best agreement with available field observation data. For this reason, the wave energy resources are evaluated based on the data hindcasted using the JMA wind field. It is found that the overall magnitudes of wave energy are larger in winter than in summer. The wave energy in August, however, is comparable to the mean wave energy during winter because of the influence of frequent high wave events caused by typhoons. The highest monthly average wave power around Yellow Sea, South Sea, East Sea, and Jeju Island are 13.3, 18.2, 13.7, and 40 kW/m, respectively.
Recently, the regulations on the use of fossil fuels have been made more stringent because of worsening global warming and pollution. To resolve these problems, renewable energies derived from solar heat and tidal currents and waves are suggested as appropriate alternatives to fossil fuels. In this regard, various research works have been conducted in oceans because wide spaces are available, and the potential threat to human lives is lower than when these studies are conducted on land. For the successful production of these renewable energies in oceans, however, it is necessary to understand the capacity of available power before the infrastructures are built; thus, advanced thorough investigations are crucial. For example, in the case of wave power generation, a precise comprehension of the distributions and variation patterns of wave energy and other wave characteristics in a region of interest is imperative. This is difficult to achieve, however, because of the high variability and dispersion of wave fields over time and space. To determine wave characteristics at a specific location, observational data must be gathered at different points in the area over sufficiently long periods (at least 10 years). In most ocean areas, however, such data are not available. An alternative approach to understand wave characteristics in specific regions is hindcasting. This technique employs wave models by which previous wave conditions have been represented and improved through comparisons with observational data.
Previous studies on hindcast modeling for regional wave conditions have been reported. Bernardino et al. evaluated wave energy resources in the Cape Verde Islands in the Atlantic Ocean west of South Africa for 10 years (2004–2013) [1]. The Simulating WAves Nearshore (SWAN) [2] wave model was used, and the European Centre for Medium-Range Weather Forecasts (ECMWF) wind field was employed for wave forcing. The ERA-Interim (ECMWF reanalysis) dataset, which is a global atmospheric reanalysis from 1979 to 2019, was utilized with a 1° spatial resolution and a 6-h temporal resolution. In the Black Sea, the wave energy distribution over a 30-year period (1987–2016) was estimated using SWAN [3]. In this study, the U.S. NCEP–CFSR (National Centers for Environmental Prediction–Climate Forecast System Reanalysis) wind data are compared with ERA-Interim data. The NCEP–CFSR winds are finally applied to the wave model because of their better performance. Wave energy distributions have also been hindcasted using SWAN in the Persian Gulf [4], coastal regions of Portugal [5], and Iroise coasts of France [6]. The analyses in these studies are similar except for differences in the applied wind fields and hindcast durations. In the Persian Gulf, ECMWF ERA-40 data with a 0.2° resolution were used for 25 years from 1984 to 2005. In Portugal, ERA-Interim data with a 0.75° resolution were employed for 15 years from 2000 to 2014. In Iroise, France, ALADIN (Aire Limitée, Adaptation dynamique, Développement InterNational, Météo-France) [7], with a 10-km special resolution and 3-h time interval wind data, was employed for eight years from 2004 to 2011. In China, the wave energy field was hindcasted for 20 years from 1996 to 2015 [8]. In this study, CCMPV2.0 (Cross-Calibrated Multi-Platform Version 2.0) wind data [9] with a 0.25° resolution are utilized to calculate wave energy distributions in 17 regions, which are classified according to their distance from the coast.
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The foregoing studies employed the SWAN wave model for hindcasting, but other similar research utilized different wave models. For example, Kim et al. evaluated the wave energy fields in the seas around the Korean Peninsula for 25 years from 1979 to 2003 [10]. In their study, various models were used at different periods according to wave conditions. Under extreme wave conditions, the wave prediction model (WAM) [11] was employed. Under normal wave conditions, the hybrid parametrical wave prediction model (HYPA) [12] was applied because of its shorter running time. The use of WAM and HYPA in this previous research, which was performed a decade ago, has motivated the conduct of the present study. Although SWAN has been used in most previous studies related to regional wave hindcasting, it has not been employed in the seas around the Korean Peninsula. The use of SWAN for hindcasting in this region is accordingly proposed in this paper. Another main objective of this study is to evaluate the wind fields used for wave forcing. According to previous studies, hindcasting accuracy depends on wind field selection. In the research of Kim et al. [10], only ECMWF data were used. In the present study, the performances of three different sets of wind data are tested through comparisons with updated observation data from the Wave Information Network of Korea (WINK, http://wink.kiost.ac.kr) system [13] to select the most suitable wind field for wave hindcasting. Additionally, although only the wave energy was previously evaluated in [10], the distributions of other wave parameters, such as wave height, period, and direction, are calculated in this study to increase the quantity of available data for researchers and other potential users.
Three wind products using the three wind datasets of the ECMWF, NCEP, and JMA are evaluated for wave hindcasting. The ECMWF and NCEP datasets are global products, whereas the JMA (Japan Meteorological Agency) dataset is a regional product for East Asia. The spatial resolutions of ECMWF, NCEP, and JMA are 0.125°, 0.205°, and 0.0625°, respectively. The time interval of ECMWF is 6 h, and that of NCEP and JMA is 1 h; wind product details are listed in Table 1.
In this study, the data employed for model evaluation are obtained from WINK because this system provides quality-controlled observational wave data from 32 stations (16, 6, and 10 stations are monitored by Korea Meteorological Administration (KMA), Korea Hydrographic and Oceanographic Agency (KHOA), and Ministry of Oceans and Fisheries (MOF), respectively). The locations of these stations and other salient details are shown and listed in Figure 1 and Table 2, respectively.
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The evolution of the action density N(E/σ, where E is the wave energy density distributed over intrinsic frequencies (σ) and propagation directions (θ)) is governed by the action balance equation.
The quantities Cσ and Cθ are the propagation velocities in spectral space (σ, θ). The right-hand side contains Stot, which is the source/sink term that represents all physical processes which generate, dissipate, or redistribute wave energy.
These terms denote, respectively, wave growth by the wind, nonlinear transfer of wave energy through three-wave interactions [14] and wave decay due to whitecapping, bottom friction and depth-induced wave breaking. The energy transfer from wind to waves (Sin) and wave energy dissipation caused by whitecapping (Swc) are approached with the saturation-based model of Westhuysen [15] combined with the wind input formulation proposed by Yan [16]. The energy dissipation by bottom friction (Sbot) is computed according to the formulation developed by Madsen et al. [17]. The energy dissipation due to wave breaking (Sbrk) according to Battjes and Janssen [18].
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In this study,
The evolution of the action density N(E/σ, where E is the wave energy density distributed over intrinsic frequencies (σ) and propagation directions (θ)) is governed by the action balance equation.
The quantities Cσ and Cθ are the propagation velocities in spectral space (σ, θ). The right-hand side contains Stot, which is the source/sink term that represents all physical processes which generate, dissipate, or redistribute wave energy.
These terms denote, respectively, wave growth by the wind, nonlinear transfer of wave energy through three-wave interactions [14] and wave decay due to whitecapping, bottom friction and depth-induced wave breaking. The energy transfer from wind to waves (Sin) and wave energy dissipation caused by whitecapping (Swc) are approached with the saturation-based model of Westhuysen [15] combined with the wind input formulation proposed by Yan [16]. The energy dissipation by bottom friction (Sbot) is computed according to the formulation developed by Madsen et al. [17]. The energy dissipation due to wave breaking (Sbrk) according to Battjes and Janssen [18].
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In this study,
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