AI-based Correction of Wave Forecasts Using the Transformer-enhanced UNet Model

Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products, thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity. The traditional method that relies on forecasters’ subjective correction of station observation data for forecasting has been unable to meet the practical needs of refined forecasting. To address this problem, this paper proposes a Transformer-enhanced UNet (TransUNet) model for wave forecast AI correction, which fuses wind and wave information. The Transformer structure is integrated into the encoder of the UNet model, and instead of using the traditional upsampling method, the dual-sampling module is employed in the decoder to enhance the feature extraction capability. This paper compares the TransUNet model with the traditional UNet model using wind speed forecast data, wave height forecast data, and significant wave height reanalysis data provided by ECMWF. The experimental results indicate that the TransUNet model yields smaller root-mean-square errors, mean errors, and standard deviations of the corrected results for the next 24-h forecasts than does the UNet model. Specifically, the root-mean-square error decreased by more than 21.55% compared to its precorrection value. According to the statistical analysis, 87.81% of the corrected wave height errors for the next 24-h forecast were within ±0.2 m, with only 4.56% falling beyond ±0.3 m. This model effectively limits the error range and enhances the ability to forecast wave heights.