A transformer-based method for correcting daily SST numerical forecasting products

This study introduces applies a Transformer-based method to correct daily Sea Surface Temperature (SST) numerical forecasting products, addressing persistent challenges in short-term SST prediction. The proposed approach utilizes a Transformer model architecture to capture complex spatiotemporal dependencies in SST error fields, enabling efficient prediction of forecast errors across multiple time scales. The method was applied to SST hindcast data from the First Institute of Oceanography (FIO-COM) ocean forecasting system, focusing on the northwestern Pacific region. Results demonstrate significant improvements in forecast accuracy, with Root Mean Square Error (RMSE) reductions ranging from 38.8% for day 2 forecasts to 17.6% for day 5 forecasts. Spatial analysis reveals the method’s robust performance across diverse oceanographic regimes, including complex coastal and shelf regions where traditional models often struggle. The Transformer model showed the ability to capture and reproduce error patterns, effectively addressing both large-scale systematic biases and smaller-scale regional variations. The consistent performance across different forecast horizons suggests potential for extending the reliable forecast range of SST predictions. The findings have important implications for applications requiring precise SST forecasts, including operational oceanography, marine weather forecasting, and coupled ocean-atmosphere modeling.