The paper addresses how the offshore wind power can be assessed based on observed wind statistics at an offshore location. Results are exemplified for a Pierson-Moskowitz wave amplitude spectrum together with mean wind speed statistics from 11 sites in the North Sea and the North Atlantic. The ambient turbulence level, for example relevant for design of the wind turbine blades, is also estimated by adopting a wind gust spectrum. The sea surface roughness is specified using a formula given in terms of significant wave height and spectral wave steepness. The present method should be useful for making initial assessments of available wind power and ambient turbulence level at offshore sites based on wind statistics.
SasakiW. Predictability of global offshore wind and wave power. Int J Mar Energy2017; 17: 98–109.
3.
UdohIEZouJ. Wind spectral characteristics on strength design of floating offshore wind turbines. Ocean Systems Eng2018; 8(3): 281–312.
4.
UdohIEZouJ. Wind spectral characteristics on fatigue responses of towerbase and moorings of floating offshore wind turbine. Ocean Systems Eng2019; 9(2): 191–218.
5.
KalverlaPCSteeneveldG-JRondaRJ, et al. An observational climatology of anomalous wind events at offshore meteomast IJmuiden (North Sea). J Wind Eng Ind Aerodynamics2017; 165: 86–99.
6.
LiLGaoZMoanT. Joint distribution of environmental condition at five European offshore sites for design of combined wind and wave energy devices. J Offshore Mech Arctic Eng2015; 137: 031901.
7.
HornJ-THKrokstadJRAmdahlJ. Joint probability distribution of environmental conditions for design of offshore wind turbines. In: Proc 36th Int Conf on Ocean Offshore and Arctic Engng, Trondheim, Norway, 2017, Paper No. OMAE2017-61451.
8.
MyrhaugDLeiraBJChaiW. Application of a sea surface roughness formula using joint statistics of significant wave height and spectral wave steepness. J Ocean Eng Mar Energy2020; 6: 91–97.
9.
CharnockH. Wind stress on a water surface. Q J R Meteorol Soc1955; 81: 639–640.
10.
JonesISFTobaY. Wind stress over the ocean. Cambridge: Cambridge University Press, 2001.
11.
ZhaoDLiM. Dependence of wind stress across an air-sea interface on wave states. J Oceanogr2019; 75(3): 207–223.
12.
PowellMDVickeryPJReinholdTA. Reduced drag coefficient for high wind speeds in tropical cyclones. Nature2003; 422: 279–283.
13.
MyrhaugD. Comments regarding “Dependence of wind stress across an air–sea interface on wave states” by D. Zhao, M. Li. J Oceanogr2020; 76(3): 243–246.
14.
TaylorPKYellandMJ. The dependence of sea surface roughness on the height and steepness of the waves. J Phys Oceanogr2001; 31: 572–590.
15.
TakagakiNKomoriSSuzukiN, et al. Strong correlation between the drag coefficient and the shape of the wind sea spectrum over a broad range of wind speeds. Geophys Res Lett2012; 39: L23604.
16.
OchiMKShinYS. Wind turbulent spectra for design considerations of offshore structures. In: Proc 20th Offshore Tech Conf, Houston, Texas, 1988, Paper No. 5736 1, pp.461–467.
TuckerMJPittEG. Waves in ocean engineering. Amsterdam, The Netherlands: Elsevier, 2001.
19.
JohannessenKMelingTSHaverS. Joint distribution for wind and waves in the Northern North Sea. In: Proceedings of the 11th offshore and polar engineering conference, Stavanger, Norway, 2001, vol. 3, pp.19–28.
20.
MaoWRychlikI. Estimation of Weibull distribution for wind speeds along ship routes. Proc IMechE, Part M: J Engineering for the Marine Environment2017; 231(2): 464–480.
21.
LøvsethJHeggemT. Wind description for coastal and offshore structures. In: GjørvOESakaiKBanthiaN, et al. (eds) Concrete under severe conditions 2 environmental and loading. London: E & FN Spon, 1998, pp.1979–1988.
22.
MyrhaugD. Wind gust spectrum over waves: Effect of wave age. Ocean Eng2007; 34(2): 353–358.
23.
MyrhaugDOngMC. Effect of wave age on wind gust spectra over wind waves. J Offshore Mech Arctic Eng2009; 131(3): 034501.
24.
AndersenOJLøvsethJ. The Frøya database and maritime boundary layer wind description. Mar Struct2006; 19(2–3): 173–192.
