This study evaluates and calibrates wind products derived from Global Navigation Satellite System Reflectometry (GNSS-R) using data from the FengYun-3E (FY-3E) global navigation satellite system occultation sounder Ⅱ (GNOS-Ⅱ) and Tianmu-1 missions. The research highlights the significance of remote sensing for the accurate measurement of sea surface wind speeds in nearshore areas, which are crucial for environmental monitoring and climate studies. Initial comparisons with National Data Buoy Center (NDBC) measurements revealed root – mean – square errors (RMSE) of 2.49 m/s for FY-3E GNOS-Ⅱ Beidou navigation satellite system (BDS) signals and 2.13 m/s for global positioning system (GPS) signals. For the Tianmu-1 mission, the RMSE values were 3.21 m/s for BDS, 3.13 m/s for GPS, 2.91 m/s for GLONASS (GLO), and 2.91 m/s for Galileo (GAL) signals. To improve accuracy, especially in the complex nearshore environments, a deep learning calibration model incorporating residual blocks was employed. This model significantly enhanced the performance compared to a basic neural network. An ablation study confirmed that including residual blocks reduced RMSE by over 20% across all signal types. The calibrated model achieved substantial accuracy improvements in the test set, reducing RMSE to 1.03 m/s for FY BDS (improvement of 60%), 0.99 m/s for FY GPS (improvement of 54%), 1.57 m/s (improvement of 51%), 1.36 m/s for Tianmu-1 GPS (57% improvement), 1.26 m/s for Tianmu-1 GLO (improvement of 56%), and 1.50 m/s for Tianmu-1 GAL (improvement 47%).

This paper presents an advanced graph convolutional network model, enhanced with Wasserstein distance-based adversarial learning (WD-ACGN), addressing the limitations of existing single-station and less explored multi-station water level forecasting approaches. The model features a novel coupled module for effectively capturing short and long-term dependencies, and a hybrid distance-based adaptive graph learning approach for spatial dependencies. This spatial dependency analysis enables rapid deployment in different coastal settings. Adversarial learning with gradient penalty further refines the model’s performance. Our model, applied to datasets from China’s Zhejiang coast and Daya Bay, outperforms baselines with a notable 12-h average root mean square error of 6.77 cm at 16 Zhejiang stations, proving its efficacy in varied maritime environments. Ablation studies validate the contribution of each model component, highlighting their collective impact on overall efficacy. Notably, the model showcases robustness in tropical cyclone scenarios and reliable results when tested with real-world observational data, underlining its potential for versatile applications in ocean engineering.

Spaceborne synthetic aperture radar (SAR) can provide unique capabilities to measure ocean surface winds under tropical cyclones (TCs), on synoptic scales, and at a very high spatial resolution. In this paper, we first discuss the accuracy and reliability of SAR-retrieved TC marine winds. The results show that wind retrievals from SAR images are in good agreement with Stepped Frequency Microwave Radiometer (SFMR) measurements, with root-mean-square error (RMSE) and correlation coefficient (CC) of 3.52 m s⁻¹ and 0.91, respectively. Based on the marine winds retrieved from SAR images, a relatively simple method is applied to extract the storm intensity (maximum wind speed) and wind radii (R34, R50, and R64) from 234 cross-polarized SAR images, in the Northwest Pacific Ocean from 2015 to 2023. The SAR-retrieved TC wind radii and intensities are compared with the best-track reports, with RMSEs for R34, R50, and R64 being 48.32, 41.88, and 38.51 km, and CCs being 0.87, 0.83, and 0.65, respectively. In terms of TC intensity, the RMSE and bias between SAR estimates and best-track data are 7.32 and 0.38 m s⁻¹, respectively. For TC Surigae (2023), we found that employing a combination of multiplatform SARs, acquired within a short time interval, has the potential to simultaneously measure the intensity and wind structure parameters. In addition, for a storm with a long life cycle, the multitemporal synergistic SARs can be used to investigate fine-scale features of the TC ocean winds, as well as the evolution of TC surface wind intensities and wind structures.