Ocean Surface Wind Speed Estimation From GNSS-R Data Using Physics-Informed Attention-Aided Convolutional Neural Network
Xin Qiao, Weimin Huang
Abstract
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 post-processing step is designed to enhance consistency in wind speed retrieval. Extensive experiments using Cyclone Global Navigation Satellite System (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.