Harnessing the powerful learning and modeling capabilities of artificial intelligence, this study introduces a deep learning-driven wind data quality control algorithm that employs correlation verification rules. By constructing a Dual-Track Information Fusion Network (DTF-Net), it captures local temporal variations in wind speed via the temporal track and uncovers physical coupling relationships among temperature, pressure, w...

Accurate prediction of Significant Wave Height (SWH) is vital for marine engineering safety, yet balancing computational efficiency with physical consistency in long-sequence modeling remains a challenge for data-driven approaches. To address this, we propose the Physics-Guided Dynamic Graph Mamba Network (PG-DyMamba). By integrating oceanographic priors such as windwave relations, our Physics-Aware Graph Learner adaptively ca...