AUWave: A Data-Driven Model for Reconstructing Significant Wave Heights Using Sparse Observations

תקציר מקורי באנגליתarXiv:2509.19384v2 Announce Type: replace-cross Abstract: Reconstructing high-resolution regional significant wave height (SWH) fields from sparse buoy observations is a critical challenge for ocean monitoring. We introduce AUWave, a hybrid deep learning framework that fuses a station-wise encoder with a multi-scale U-Net enhanced by self-attention to recover regional SWH fields. Trained and validated using NDBC buoy observations and ERA5 reanalysis over the Hawaii region, AUWave achieves high accuracy. It consistently outperforms a representative baseline, especially in configurations with more than a single buoy, demonstrating the benefit of its multi-scale architecture. Spatial error analysis shows performance is highest near observation sites, as expected. Further, buoy ablation studie