Fusion of multi-source wave spectra based on BU-NET

The wave spectrum describes the distribution of wave energy across frequency and direction. Obtaining wave spectrum information with high accuracy is of great value for oceanographic research and disaster prevention and reduction. Currently, wave spectral data can be obtained from remote sensing observations, global meteorological and climate reanalysis products, and in-situ observations, which exhibit different advantages and limitations in terms of spatio-temporal resolution, accuracy, and data coverage. Fusing these diverse spectral data to complement the advantage of improving the accuracy of wave spectrum is very promising. However, there is still no simple and effective method to fuse the above spectral data. In this study, a multi-source spectral fusion method is developed based on BU-NET, which realizes the integration of ERA5 spectra and SWIM spectra, with buoy spectra as the reference. The results of the systematic evaluation indicate that the fusion spectra alleviate parasitic peaks, address the issue of larger mean energy, and compensate for energy loss due to the cutoff frequency in the SWIM spectra. The fusion spectra also alleviate energy underestimation during high sea states in the ERA5 spectra. Furthermore, the accuracy of the significant wave height, mean wave period, dominant wave period, and dominant wave direction obtained from the fusion spectra is improved. The root mean square errors between these parameters from the fusion spectra and those from buoy spectra are 0.217 m, 0.378 s, 1.599 s, and 33.094°, respectively. • A multi-source wave spectrum data fusion method based on deep learning is developed. • This method combines the strengths of multi-source wave spectrum data. • The fusion spectra is closer to the buoy spectra. • Alleviate parasitic peaks and uneven spectral energy transitions.