Improvements to the CFOSAT SWIM Wave Spectrum Based on the ViT Deep Learning Model

The Surface Wave Investigation and Monitoring (SWIM) aboard the China-France Oceanic Satellite (CFOSAT) provides the ocean wave spectrum (70-500 m wavelength range). However, the accuracy of this data is affected by speckle noise, low-frequency parasitic peaks, and missing information in the short wavelength range. To improve the accuracy of the SWIM wave spectrum, this letter introduces a Vision Transformer deep learning model combined with a deconvolution block, which leverages buoy wave spectrum and full wavenumber wind wave spectrum to improve the SWIM wave spectrum with high precision and wide wavelength range. The results show that the linear correlation coefficient of the improved wave spectrum has increased from 0.510 to 0.833. Furthermore, the accuracy of spectrum parameters is enhanced. Particularly, compared with the original SWIM spectrum, the root mean square error (RMSE) for the mean wave period (MWP) and peak wave period (PWP) decreased by 70.19% and 71.68%, respectively.