|Spectral partitioning and identification of wind sea and swell|Portilla, J.; Ocampo-Torres, F.J.; Monbaliu, J. (2009). Spectral partitioning and identification of wind sea and swell. J. Atmos. Oceanic. Technol. 26(1): 107-122. dx.doi.org/10.1175/2008JTECHO609.1
In: Journal of Atmospheric and Oceanic Technology. American Meteorological Society: Boston, MA. ISSN 0739-0572, more
|Authors|| || Top |
- Portilla, J., more
- Ocampo-Torres, F.J.
- Monbaliu, J., more
In this paper, different partitioning techniques and methods to identify wind sea and swell are investigated, addressing both 1D and 2D schemes. Current partitioning techniques depend largely on arbitrary parameterizations to assess if wave systems are significant or spurious. This makes the implementation of automated procedures difficult, if not impossible, to calibrate. To avoid this limitation, for the 2D spectrum, the use of a digital filter is proposed to help the algorithm keep the important features of the spectrum and disregard the noise. For the 1D spectrum, a mechanism oriented to neglect the most likely spurious partitions was found sufficient for detecting relevant spectral features. Regarding the identification of wind sea and swell, it was found that customarily used methods sometimes largely differ from one another. Evidently, methods using 2D spectra and wind information are the most consistent. In reference to 1D identification methods, attention is given to two widely used methods, namely, the steepness method used operationally at the National Data Buoy Center (NDBC) and the Pierson-Moskowitz (PM) spectrum peak method. It was found that the steepness method systematically overestimates swell, while the PM method is more consistent, although it tends to underestimate swell. Consistent results were obtained looking at the ratio between the energy at the spectral peak of a partition and the energy at the peak of a PM spectrum with the same peak frequency. It is found that the use of partitioning gives more consistent identification results using both 1D and 2D spectra.