A Coordinate-Based Framework for Sea Surface Wind Speed Reconstruction from Sparse Multi-Source Observations

Accurate sea surface wind speed fields are essential for marine navigation, offshore operations, and air–sea interaction studies. However, limited communication bandwidth makes it difficult to receive forecasts from land-based centers, motivating wind speed reconstruction using sparse observations. To address this challenge, we propose SwiftWind, a coordinate-based deep learning framework for sea surface wind speed reconstruction at arbitrary locations by fusing multi-source observations. SwiftWind embeds non-gridded, variable-length observations through adaptive latent representations and latitude–longitude coordinate encoding. We conduct Observing System Simulation Experiments (OSSEs), real-world observational experiments, and arbitrary-location inference experiments. Under ERA5-based evaluation, SwiftWind consistently outperforms existing data-driven baselines, including Fourier Neural Operator (FNO) and Vision Transformer (ViT) models, demonstrating robustness to observation number, noise level, and spatial distribution. Compared to the GFS 6 h forecast fields, SwiftWind achieves approximately 20–23% reductions in RMSE and 19–22% reductions in MAE under real-world observational settings. In independent buoy validation, SwiftWind performs comparably to ViT and slightly worse than FNO, likely due to differences in scattered-point processing and buoy distribution. These findings indicate that SwiftWind is suitable for near-real-time onboard wind speed reconstruction under sparse-observation conditions.