Ocean Surface Wind Speed Estimation From GNSS-R Data Using Physics-Informed Attention-Aided Convolutional Neural Network

Accurate global ocean surface wind speed estimation is crucial for weather forecasting and maritime transportation. Traditional retrieval methods based on Global Navigation Satellite System Reflectometry (GNSS-R) data and deep learning techniques often struggle to capture the complex, nonlinear relationships between GNSS-R observables and wind speed. This challenge is further exacerbated by imbalanced data distributions, which lead to significant underestimation under high wind conditions (15–30 m/s, here). To mitigate these issues, this study proposes a novel Physics-Informed Attention-Aided Convolutional Neural Network (PA-CNN). The proposed model incorporates an attention mechanism into the CNN architecture to adaptively focus on the most informative features. In addition, geophysical principles related to GNSS-R signal scattering are integrated with data-driven learning, improving both the interpretability and generalization capability of the network. Moreover, a spatial–temporal smoothing postprocessing step is designed to enhance consistency in wind speed retrieval. Extensive experiments using Cyclone GNSS (CYGNSS) datasets demonstrate that PA-CNN with smoothing outperforms existing deep learning approaches, achieving an overall root mean square difference (RMSD) of 1.38 m/s compared with ERA5 reanalysis data and 1.56 m/s against buoy measurements, highlighting the potential of combining physics-informed modeling with advanced deep-learning techniques for improved ocean surface wind speed retrieval.