A Gated Cross‐Scale Residual Fusion Method for Multivariate Ocean Wave Forecasting

ABSTRACT

Accurate multivariate ocean wave forecasting requires representing rapidly evolving local dynamics, longer‐term background variation, and dependencies among sea‐state variables. This study proposes a Gated Cross‐Scale Residual Fusion Network (GCRF‐Net) for multivariate one‐step ocean wave forecasting. The 120‐step short‐window main branch uses long short‐term memory (LSTM), convolutional temporal, and attention encoders to form the base prediction. A 960‐step auxiliary LSTM branch generates a candidate residual correction. A variable‐wise gate conditioned on the main‐scale and long‐scale representations then regulates this correction. Under a station‐specific evaluation protocol, experiments on three independently processed Queensland wave‐buoy stations showed that GCRF‐Net achieved the lowest mean one‐step mean squared error (MSE) among the evaluated models at each station. Direct multi‐horizon results were horizon‐ and station‐dependent. This finding suggests that the model is most directly suited to the intended one‐step (0.5 h) nowcasting task. Ablation studies supported the contributions of multi‐view main‐scale representation learning and gated residual correction. Graphics processing unit (GPU) benchmarks showed that the dual‐window architecture incurred additional latency and memory relative to lightweight single‐window baselines. Overall, the results suggest that using long‐window information as a gated residual correction can improve performance in the intended one‐step (0.5 h) multivariate ocean wave nowcasting task while preserving the dominant role of recent observations.