Calibration of Omnidirectional Wave Height Spectra by SWIM Through a BU-Net

Surface waves investigation and monitoring (SWIM) can provide global wave spectra, but under small sea conditions, the presence of parasitic peaks at low wavenumbers, surfboard effects, and residual speckle noise lead to performance degradation of SWIM wave height spectrum products. To reduce the impacts of the above factors on the SWIM wave height spectrum, in this article, a convolution neural network (CNN) method based on BU-Net is proposed for calibrating SWIM omnidirectional wave height spectra with buoy measurements under sea states (wind wave mainly/swell mainly) and sea surface conditions (wind speed from 9 to 19 m/s, and significant wave height (SWH) from 0.8 to 3.4–4.2 m). The calibration results show that the impact of the above factors on the SWIM omnidirectional wave height spectrum can be corrected. The correlation coefficients between the corrected SWIM beams 6°, 8°, and 10° and the buoy mean omnidirectional wave height spectrum are all greater than 0.90, and the relative error of the peak wavenumber is within 10%. The relative error of the integrated energy is mostly less than 20%. In addition, the performance of spectral integration parameters (effective wave height $H_{s}$ , and energy wave period $T_{m-10}$ ) of each spectral beam of SWIM has been verified using Meteo-France WAve Model (MFWAM) reanalysis data. The validation results show that RMSE of $H_{s}$ and $T_{m-10}$ , for the corrected SWIM beam 6° (8°, 10°) under wind wave sea conditions are 0.31 m (0.32, 0.25 m) and 0.50 s (0.51, 0.49 s), respectively; those for swell cases are 0.16 m (0.16, 0.13 m) and 0.87 s (0.77, 0.72 s), respectively.