Wave spectral partitioning for the SWIM spectrometer based on the wave age and parameter optimization method

Abstract

The paper proposes a wave spectral partitioning method for the surface waves investigation and monitoring (SWIM) spectrometer based on the wave age and parameter optimization (WA-PO) method. The wave age criterion is a way to identify waves as wind waves or swells, and parameter optimization methods enable the modeling of wave spectra. The combination of the two can use the spectrum models to fit the SWIM spectra based on clarifying whether the spectrum models belong to wind waves or swells, which achieves spectral partitioning. The WA-PO method consists of four steps: peaks region dividing, spectral peaks searching, objective function constructing, and model parameters optimizing. The results show that compared with the partition products provided by the SWIM, the partition parameters obtained from the WA-PO method are closer to those of the Integrated Ocean Waves for Geophysical and other Applications (IOWAGA) numerical hindcast dataset developed from the WAVEWATCH III (WW3). The WA-PO method can prevent over-partitioning of the wave spectra and detect wind-wave components hidden in the swell partition.

Keywords:

• Parameter optimization
• SWIM
• wave age
• wave spectral partitioning

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The SWIM dataset is available from the following website: https://www.aviso.altimetry.fr/en/data/products/wind/wave-products/wave-wind-cfosat-products.html.

The IOWAGA dataset is available from the following website: https://www.umr-lops.fr/Donnees/Vagues/sextant.