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Domenii publicaţii > Stiinte ingineresti + Tipuri publicaţii > Articol în volumul unei conferinţe
Autori: Rusu, L., Bernardino, M., Guedes Soares, C
Editorial: AMER SOC MECHANICAL ENGINEERS, Proceedings of the 27th International Conference on Offshore Mechanics and Arctic Engineering (OMAE2008), VOL 4, p.637-644, 2008.
Rezumat:
The influence of the wind field resolution in the accuracy of the wave predictions is studied using spectral numerical models in highly non-stationary situations. The main area of interest is the port of Sines, located in the Portuguese continental coastal environment south of Lisbon.
An implementation of the MM5 atmospheric model was developed for the area of study, starting from a large area of the Atlantic Ocean and nesting successively with finer grid towards the point of interest, so as to provide wind fields with increasing level of detail.
The SWAN model was forced with wind fields simulated by different models and with different resolutions. A wave rider type directional buoy located offshore the Sines port at approximately 100 meters water depth was used as check point. In a first approach, two different wind fields simulated by REMO and MM5 were considered for forcing the SWAN based transformation module. Both wind fields have a spatial resolution of 0.5 degrees and were obtained using the global NCEP reanalysis data as wind driver. Initial wave boundary conditions are provided by WAM simulations for the entire North Atlantic basin. Afterwards, three successive SWAN areas were implemented. The three spatial resolutions defined for these SWAN computational domains were 0.05, 0.02 and 0.005 degrees. Corresponding special resolutions for the wind fields produced by MM5 ranged from 0.15 to 0.015 degrees.
Simulations were performed from the beginning of December 2000 till the end of February 2001, which is one of the most energetic periods registered close to this coast and the results in terms of significant wave height, mean period and wave direction were compared with the in situ measured data.
Cuvinte cheie: WAM model, hindcasts