A coupled Swin transformer-LSTM network for high-resolution ocean wave forecasting: A reanalysis-driven skill assessment in the Chinese marginal seas

作者：Yongqiang Liu, Delei Li, Xiang Gong, Jianlong Feng, Hailong Liu, Ji Qi, Baoshu Yin · 发表于：Journal of Sea Research · 年份：2026 · DOI：10.1016/j.seares.2026.102670 · 被引用次数：2 · 研究领域：Ocean Waves and Remote Sensing、Oceanographic and Atmospheric Processes、Hydrological Forecasting Using AI

Accurate wave forecasting is essential for maritime safety and provides crucial scientific guidance for coastal operations and planning. Most artificial intelligence-based wave forecast models were conducted at coarse resolutions, e.g., 0.25° or 0.5° spatial resolution, and struggled to maintain high forecasting accuracy for extended periods. Here, we introduce the coupled Swin Transformer-LSTM network (SwinLSTM), a hybrid architecture designed to make a spatiotemporal forecast of significant wave height (SWH) at a 0.1-degree resolution over 72-h lead-time in the Bohai Sea, Yellow Sea, and East China Sea. In this study, both historical and lead-time wind fields are taken from the ERA5 reanalysis; therefore, the reported skill reflects a reanalysis-driven (hindcast-style) evaluation that provides an upper-bound estimate under near-perfect wind forcing. The SwinLSTM architecture effectively captures spatial dependencies, simultaneously extracting both long-term and short-term spatiotemporal dependencies in ocean wave dynamics for efficient two-dimensional spatial forecasting. Through sensitivity experiments, the optimal configuration was determined, with historical wind, SWH, topography, and ERA5 reanalysis future wind (used here as a proxy forcing for lead-time prediction) identified as the optimal input combinations using a 6-h encoding time step. Based on comprehensive model evaluation with this optimal configuration, our results demonstrate that for forecast horizons of 1-,...