Hybrid CNN-Transformer Network With a Weighted MSE Loss for Global Sea Surface Wind Speed Retrieval From GNSS-R Data

Global Navigation Satellite System Reflectometry (GNSS-R) plays a crucial role in sea surface wind speed measurement, and convolutional neural networks (CNNs) have been a widely used method for wind speed retrieval from GNSS-R data. However, CNNs have limitations in global feature extraction due to the fixed convolutional kernels. Moreover, current studies on wind speed retrieval from GNSS-R data exhibit significant overfitting at low wind speed and underestimation at high wind speed due to the extremely imbalanced data distribution. To address these issues, a hybrid CNN-Transformer Network (CTN) with a weighted mean square error (MSE) loss is proposed in this study. Specifically, the designed CTN incorporates CNN and transformer encoder blocks to capture local and global features, with a dedicated feature fusion module to integrate these features. In addition, a novel weighted MSE loss function is designed to tackle the issue of imbalanced data distribution and enhance the estimation accuracy at high wind speeds. The proposed method is validated on Cyclone GNSS (CYGNSS) data and demonstrates improved performance compared with other machine learning algorithms. It achieves a root mean square difference (RMSD) of 1.417 m/s compared to the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and 1.620 m/s compared to the buoy data from National Data Buoy Center (NDBC). Notably, the proposed model trained with the weighted MSE loss function shows an improvement of 20.3% in RMSD for samples with wind speeds exceeding 15 m/s, highlighting the effectiveness of the designed loss function in handling high wind speed data.