Ocean surface winds play an essential role in regulating the earth’s weather and climate, and the Cyclone GNSS (CYGNSS) mission launched in 2016 is designed specially to monitor the ocean wind speed. In this study, an innovative model is developed based on a deep learning method to retrieve the ocean wind speed by making full use of the spatiotemporal information of CYGNSS observations. The proposed model named CNN-LSTM is established based on two modules, i.e., the Convolution Neural Network (CNN) module that extracts the spatial features around the Specular Point (SP) from a Two-Dimensional matrix of delay-Doppler Map (DDM) and the Long Short-Term Memory (LSTM) module which extracts the temporal features over a time series. The performance of the ocean wind speed derived from CNN-LSTM is assessed with the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) products. The results show that the wind speed derived from CNN-LSTM reveals an accuracy of 1.34 m/s in terms of root mean square error (RMSE) values, showing an improvement of about 36.8%, 14.6%, 6.3%, when compared to the official retrieval algorithm called Minimum Variance Estimator (MVE), Multilayer Perceptron (MLP) net, and the CNN, respectively, confirming the feasibility and effectiveness of the designed method. Among all the experiments in this study which apply machine learning-based algorithms, the wind speed achieved by CNN-LSTM presents the smallest RMSE value. Furthermore, the error analyses of the wind speed retrieval in spatial and temporal scale are also discussed, which indicate the robust performance of CNN-LSTM model. The results show that the CNN-LSTM model proposed in this study contributes to offering efficient processing of Global Navigation Satellite Systems Reflectometry (GNSS-R) observations and fully exploits the capabilities of high-accurate ocean wind speed retrieval on a global scale.

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP).

The calibration microwave radiometer (CMR) on board the Haiyang-2A (HY-2A) satellite provides wet tropospheric delay correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. The ground-based global navigation satellite system (GNSS) provides precise precipitable water vapor (PWV) with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 International GNSS Service (IGS) stations along the global coastline and 56 d shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made in the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm as the root mean square (rms) within 100 km. Geographically, the rms is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an rms of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well.

Validating the wet path delay (WPD) is essential to determining satellite microwave radiometer errors that may deteriorate sea level measurement accuracy. The global navigation satellite system (GNSS) is a favored method for assessing WPD for altimetry. Over Chinese coastal waters, however, validations have not been performed, primarily because of the large gaps between International GNSS Service sites. Herein, we report a long-term assessment of the Jason-2/3 radiometers and the initial performance of the Chinese HY-2B radiometer via comparisons of WPD values derived from three types of GNSS networks along the Chinese coast. A new method based on the second-order derivative of WPD along satellite tracks was developed to detect where land contamination first appears. The results indicate that this method is more sensitive to land influences; the distance at which land contamination first appears was estimated to be approximately 40–50 km over the China coast sea. To compare the WPD at different heights, such as GNSS sites and sea level surface, an exponential function containing the corresponding decay coefficients was adopted. The WPD decay coefficients for each GNSS site were recalculated using the 3-D ERA5 pressure level fields instead of the empirical value of 2000. The results show that the new coefficient may reduce the differences between WPD values at different heights. This WPD comparison over Chinese coastal waters indicates average WPD differences of 3 (Jason-2) and −1 mm (Jason-3), showing a high level of consistency between the GNSS sites and the satellite radiometers. The uncertainties in the WPD differences were estimated to be 14–21 and 7–24 mm for the Jason-2/3 and HY-2 radiometers, respectively, which is consistent with previous studies using global GNSS. After removing the GNSS uncertainty and the spatial decorrelation, the WPD uncertainties of the Jason-2, Jason-3, and HY-2 radiometers over the regional area were estimated to be 13, 14, and 11 mm, respectively. The long-term evaluation of the WPD differences with the GNSS sites indicates that the drift rates of the Jason-2/3 radiometers over China coast waters are 0.3 and −0.9 mm/y, respectively.