A new non-parametric correction model and its applications to hindcasting wave data

Wang, Lvqing; Liang, Bingchen; Li, Huajun

doi:10.1016/j.oceaneng.2017.01.010

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…The essence of them is that no restrictions are imposed on the spectral shape, which is fully determined by the source terms. WW3 is more suits for wave energy simulation in large and mesoscale sea areas (Bernier et al, 2016;Charles et al, 2012;Wang et al, 2017). On the contrary, SWAN is widely used in many coastal wave studies since some nearshore wave evolution processes have been incorporated into the model with parameterized terms, such as depth induced wave breaking and triad wave interactions (Akpinar and K€ omu¨rcu¨,2012;Akpinar et al, 2012;Cherneva et al,2008;Iglesias and Carballo,2010;Liang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Long-term assessment of wave energy in the China Sea using 30-year hindcast data

Yang

Fan

Duan

et al. 2019

Energy Exploration & Exploitation

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In this study, a long-term assessment of the wave energy in the China Sea was performed for a 30-year time interval (1988–2017), using the model WAVEWATCH-III. The reliability of the wave simulation results was increased by means of longer time horizon data compared to other relevant studies in the China Sea. This analysis provided information on the regional distribution as well as on the monthly and seasonal variability. The exploitation and stability of wave energy were taken into consideration, so as to find the advantage of resource exploitation. Results indicated that values of significant wave height and wave power density had obviously differences compared with different months, especially in December with a maximum significant wave height of 2.7 m and 35 kW/m of wave power density. The minimum value of them appeared in May, was 1.0 m and 4.5 kW/m, respectively. The distribution of wave energy was abundant in winter and the poorest in summer. In winter, the significant wave height in most areas was above 1.8 m, while the maximum wave energy density in summer was only 1.2 m. As for the wave power density, in winter values in most areas were above 18 kW/m, while the maximum value in summer was only 12 kW/m. In sight of regional distribution, the highest wave energy potential was located in the Northern South China Sea, the East China Sea, the Ryukyu Islands waters, east of the Taiwan Island and the Luzon Strait, with coefficient of variation was within 2.0 and occurrence of exploitation was above 80%, whereas the Bohai Sea, the northern part of the Yellow sea, the Gulf of Thailand, and the Northern Bay were in poor contribution, with occurrence of exploitation was within 50%.

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Section: Introductionmentioning

confidence: 99%

Long-term assessment of wave energy in the China Sea using 30-year hindcast data

Yang

Fan

Duan

et al. 2019

Energy Exploration & Exploitation

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“…Before the assessment, the meteorological characteristics are analysed to identify extreme weather. In the SCS, the tropical cyclone always drives the storm wave (Anoop et al, 2015;Hithin et al, 2015;Sanil Kumar and Anoop, 2015;Ojeda et al, 2017;Wang et al, 2017;Mortlock et al, 2018;Sanil Kumar et al, 2018) and the number of tropical cyclones is sufficiently large. Thus, it is possible to study the extreme significant wave height in a tropical cyclone (Young et al, 2012;Young and Vinoth, 2013;Young, 2017).…”

Section: Introductionmentioning

confidence: 99%

Extreme significant wave height of tropical cyclone waves in the South China Sea

Shao

Liang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Extreme significant wave heights are assessed in the South China Sea (SCS), as assessments of wave heights are crucial for coastal and offshore engineering. Two significant factors include the initial database and assessment method. The initial database is a basis for assessment, and the assessment method is used to extrapolate appropriate return-significant wave heights during a given period. In this study, a 40-year (1975–2014) hindcast of tropical cyclone waves is used to analyse the extreme significant wave height, employing the peak over threshold (POT) method with the generalized Pareto distribution (GPD) model. The peak exceedances over a sufficiently large value (i.e. threshold) are fitted; thus, the return-significant wave heights are highly dependent on the threshold. To determine a suitable threshold, the sensitivity of return-significant wave heights and the characteristics of tropical cyclone waves are studied. The sample distribution presents a separation that distinguishes the high sample from the low sample, and this separation is within the stable threshold range. Because the variation in return-significant wave heights in this range is generally small and the separation is objectively determined by the track and intensity of the tropical cyclone, the separation is selected as a suitable threshold for extracting the extreme sample in the tropical cyclone wave. The asymptotic tail approximation and estimation uncertainty show that the selection is reasonable.

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“…CC BY 4.0 License. major extreme weather in the SCS, which always drives the storm wave (Anoop et al, 2015;Hithin et al, 2015; Sanil Ojeda et al, 2017;Wang et al, 2017;Mortlock et al, 2018;Sanil Kumar et al, 2018). In addition, the number of tropical cyclones is counted in the SCS, which shows that the tropical cyclone wave is feasible for studying extreme significant wave heights.…”

Section: Introductionmentioning

confidence: 99%

Study of the threshold for the POT method based on hindcasted significant wave heights of tropical cyclone waves in the South China Sea

Shao

Liang

et al. 2019

Preprint

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Abstract. An assessment of extreme significant wave heights is performed in the South China Sea (SCS), which is crucial for the coastal and offshore engineering in this area. Two significant factors influencing the assessment are the initial database and the assessing method. The initial database is a basic for assessment, and the assessing method is used to extrapolate appropriate return significant wave heights based on this database during a period. In this study, a 40-year (1975–2014) hindcasted significant wave height of tropical cyclone waves is adopted as the initial database. Based on this database, the peak significant wave height of every tropical cyclone wave is directly extracted as the initial sample; the independent and identically distributed assumption is satisfied; and the interference for the selection of the sample is avoided. The peak over threshold (POT) method with the generalized Pareto distribution (GPD) model is employed to extract the sufficiently large and high sample for model estimation. The peak excesses over a sufficiently high value (i.e., threshold) are fitted; thus, the return significant wave heights are highly dependent on the threshold. To determine the unique threshold for the POT method, characteristics of tropical cyclone waves are researched. The research results reveal that the separation value shown in the distribution of the initial sample is suitable for sampling in the SCS. Because the separation value is within the stable threshold range and the asymptotic tail approximation and estimation uncertainty are reasonable, the selected threshold is suitable and the corresponding return significant wave height is reliable.

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A new non-parametric correction model and its applications to hindcasting wave data

Cited by 8 publications

References 38 publications

Long-term assessment of wave energy in the China Sea using 30-year hindcast data

Long-term assessment of wave energy in the China Sea using 30-year hindcast data

Extreme significant wave height of tropical cyclone waves in the South China Sea

Study of the threshold for the POT method based on hindcasted significant wave heights of tropical cyclone waves in the South China Sea

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