This paper evaluates the Fengyun-3E (FY-3E) Global Navigation Satellite System Occultation Sounder II (GNOS-II) global wind speed product using measurements from 91 National Data Buoy Center (NDBC) buoys in offshore regions and 48 Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) buoys in the open ocean. The study underscores the differences in wind speed accuracy between offshore and open ocean regions, supporting the application of the GNOS-II system in various maritime environments. The findings reveal that nearshore wind speed measurements have a lower accuracy compared to those in the open ocean. Specifically, the Root Mean Square Error (RMSE) for the BeiDou Navigation Satellite System (BDS) product against NDBC buoy data is 2.494 m/s, while for the Global Positioning System (GPS) product it is 2.128 m/s. Against TAO/TRITON buoy data, the RMSE for the BDS product is 1.831 m/s, and for the GPS product, it is 1.918 m/s. These results indicate a need for further enhancement of offshore wind speed measurements to meet application requirements.

This study proposes a deep learning (DL) approach for retrieving high wind speeds during tropical cyclones using a random forest algorithm applied to RADARSAT and Sentinel-1A/B Synthetic Aperture radar (SAR) images. The effectiveness of the proposed DL-based model is then demonstrated through a comprehensive validation of the results. Statistical analysis of the results showed that the proposed method performs well, with a low root-mean-square error, mean bias, and high correlation coefficient when compared to SFMR (Stepped-Frequency Microwave Radiometer) winds. The reconstructed wind speeds and inner-core structures were found to be in good agreement with surface wind measurements from SFMR. These findings could have significant implications for improving our understanding and prediction of tropical cyclone dynamics, as well as for operational forecasting and disaster management.

Synthetic-aperture radar (SAR) plays a crucial role in monitoring the fine structure of tropical cyclones, but its effectiveness is constrained by limitations such as signal degradation and saturation. To address this challenge, we proposed a transfer learning-based generative adversarial network (GAN) framework with a dilated convolution and attention mechanism for reconstructing inner-core high winds from SAR images. We have employed the principles of transfer learning to adapt pre-trained models developed by the HWRF (Hurricane Weather Research and Forecasting model) winds to SAR images during tropical cyclone events for reconstruction. The proposed model can effectively capture the relationship between features in the low-precision areas and global features from SAR images, facilitating tropical cyclone wind speed reconstruction. The utilization of Global Precipitation Measurement (GPM) Level 3 rainfall data facilitates the identification of rainfall regions in 89 SAR images obtained from Radarsat-2 and Sentinel-1A/B missions. Comparison with Stepped Frequency Microwave Radiometer (SFMR) data reveals that the model exhibits a bias of –0.69 m/s, an RMSE of 4.08 m/s, and anRvalue of 0.91 under heavy rainfall conditions (>7.62 mm/hr). Remarkably, the GAN model exhibits excellent performance compared with measurements from the Soil Moisture Active Passive (SMAP) L-band radiometer, achieving an RMSE of 3.78 m/s. Our findings indicate that deep learning technology holds significant promise for the reconstruction and monitoring of tropical cyclones through the utilization of SAR imagery.

In this study, we propose a method for wind speed retrieval using a random forest (RF) algorithm for Cyclone Global Navigation Satellite System (CYGNSS) data. We first compared CYGNSS data with Soil Moisture Active Passive (SMAP) data and found a certain deviation in the CYGNSS ”young sea, limited fetch” (YSLF) data product for high winds. Then, we used SMAP as the ”ground truth” to train an RF model and applied it to the wind speed retrieval of CYGNSS data. The experimental results show that using the RF algorithm for wind speed retrieval can eliminate noise in the CYGNSS YSLF wind speed data and improve retrieval accuracy. In addition, we explored the impact of different input parameter combinations on model performance and found that using an 11-parameter model in CYGNSS wind speed retrieval can achieve optimal performance. This can provide valuable reference for rapid near-real-time retrieval of tropical cyclones using CYGNSS.

Synthetic aperture radar (SAR) can monitor the sea surface imprints of tropical cyclones (TCs) with high spatial resolution, day and night. Automatically locating TC center positions in SAR images is a challenging task. This article developed a two-stage, fully automatic TC-center estimation algorithm. First, the sea surface wind directions (SSWDs) at SSWD points are retrieved by the improved local gradient (ILG) method. We incrementally deflected the SSWD outward at a 0.5° angle from −50° to 10° (the negative angles represent clockwise deflection). The heat maps are generated for each of the 121 angles, and the values at each heat map are the cumulative numbers of the lines perpendicular to the compensated SSWDs. The site corresponding to the maximum cumulative number in all 121 heat maps is the coarsely estimated center position. This center search is the culmination if it falls outside the SAR image. Otherwise, the second stage is triggered, and the sub-SAR image (150 km$\times150$km) centered at the coarsely estimated center position is extracted. Then, the first-stage procedure is repeated with the sub-SAR image to precisely estimate the center position. Optionally, the precisely estimated center position can be further adjusted by considering that normalized radar cross section (NRCS) is normally minimal at the TC center. We applied the algorithm to 87 SAR images. Five of these images do not contain TC centers. The results are in good agreement with the visually located TC center positions and those in the best track (BT) datasets.

The potential mymargin for improving storm surge simulation is demonstrated by using winds derived from ground-based Global Navigation Satellite System Reflectometry (GNSS-R) that uses BeiDou geostationary Earth orbit (GEO) satellite signals. We reconstruct wind fields by blending GNSS-R coastal winds with the European Center for Median Weather Forecasts (ECMWF) reanalysis product. The reconstructed winds agree well with the weather station data collected at Yangjiang in Guangdong, China. The ECMWF winds and the reconstructed winds are used to force a storm surge model off the Chinese coast during typhoon Utor 2013, respectively. The model storm surges forced by the reconstructed winds agree substantially better with tide-gauge observations than those forced by the ECMWF winds. The average error has been reduced by 30.5% from 24.3 cm with the ECMWF winds to 16.9 cm with the reconstructed winds. This letter suggests that GNSS-R coastal winds can have a positive impact on the accuracy of storm surge hindcasting directly and forecasting indirectly by improving the initial conditions.

China launched two new spaceborne microwave sensors in August and September 2016. One is the C band multi-polarization high resolution synthetic aperture radar (SAR) on board satellite GF-3. The other is the Ku band wide swath Interferometric Imaging Radar Altimeter (InIRA) on board space laboratory TG-2. This paper gives some preliminary results for the quantitative remote sensing of ocean winds and waves from the GF-3 SAR and the TG-2 InIRA. Comparisons to the ECMWF ERA-Interim reanalysis data show good agreements but more valuable details.