This study validated wind speed (WS) and significant wave height (SWH) retrievals from the Sentinel-3A/3B and Jason-3 altimeters for the period of data beginning 31 October 2019 (to 18 September 2019 for Jason-3) using moored buoy data and satellite Meteorological Operational Satellite Program (MetOp-A/B) Advanced Scatterometer (ASCAT) data. The spatial and temporal scales of the collocated data were 25 km and 30 min, respectively. The statistical metrics of root mean square error (RMSE), bias, correlation coefficient (R), and scatter index (SI) were used to validate the WS and SWH accuracy. Validation of WS against moored buoy data indicated errors of 1.19 m/s, 1.13 m/s and 1.29 m/s for Sentinel-3A, Sentinel-3B and Jason-3, respectively. The accuracy of Sentinel-3A/3B WS is better than that of Jason-3. All three altimeters underestimated WS slightly in comparison with the buoy data. Errors in WS at different speeds or SWHs increased slightly as WS or SWH increased. Over time, the accuracy of the Jason-3 altimeter-derived WS improved, whereas that of Sentinel-3A showed no temporal dependence. The WSs of the three altimeters were compared with ASCAT wind data for validation purposes over the global ocean without in situ measurements. On average, the WSs of the three altimeters were lower in comparison with the ASCAT data. The accuracy of the three altimeters was found to be consistent and stable at low/medium speeds but it decreased when the WS exceeded 15 m/s. Validations of SWH against buoy wave data indicated that the accuracy of Jason-3 SWH was better than that of Sentinel-3A/3B. However, the accuracy of all three altimeters decreased when the SWH exceeded 4 m. The accuracy of Sentinel-3A and Jason-3 SWH was temporally stable, whereas that of Sentinel-3B SWH improved over time. Analyses of SWH accuracy as a function of wave period showed that the Jason-3 altimeter was better than the Sentinel-3A/3B altimeters for long-period ocean waves. Generally, the accuracy of WS and SWH data derived by the Sentinel-3A/3B and Jason-3 altimeters satisfies their mission requirements. Overall, the accuracy of WS (SWH) derived by Sentinel-3A/3B (Jason-3) is better than that retrieved by Jason-3 (Sentinel-3A/3B).

The South China Sea is a major shipping hub between the West Pacific and Indian Oceans. In this region, the demand for energy is enormous, both for residents’ daily lives and for economic development. Wave energy and wind energy are two major clean and low-cost ocean sources of renewable energy. The reasonable development and utilization of these energy sources can provide a stable energy supply for coastal cities and remote islands of China. Before wave energy and wind energy development, however, we must assess the potential of each of these sources. Based on high-resolution and high-accuracy wave field data and wind field data obtained by ERA-Interim reanalysis for the recent 38-year period from 1979–2016, the joint development potential of wave energy and wind energy was assessed in detail for offshore and nearshore areas in the South China Sea. Based on potential installed capacity, the results revealed three promising areas for the joint development of nearshore wave energy and wind energy, including the Taiwan Strait, Luzon Strait and the sea southeast of the Indo-China Peninsula. For these three dominant areas (key stations), the directionality of wave energy and wind energy propagation were good in various seasons; the dominant wave conditions and the dominant wind conditions were the same, which is advantageous for the joint development of wave and wind energy. Existing well-known wave energy converters (WECs) are not suitable for wave energy development in the areas of interest. Therefore, we must consider the distributions of wave conditions and develop more suitable WECs for these areas. The economic and environmental benefits of the joint development of wave and wind energy are high in these promising areas. The results described in this paper can provide references for the joint development of wave and wind energy in the South China Sea.

The HY-2B satellite was successfully launched on 25 October 2018. One of the main payloads of the HY-2B was a radar altimeter. In the present study, the quality of the HY-2B along-track sea surface heights (SSH), significant wave heights (SWH), and sea surface wind speeds (SSWS) were assessed, including their precision and accuracy. In order to achieve this goal, the mono-mission metrics of the HY-2B were analyzed and compared with those of the Jason-2 and Jason-3 over the same periods of time. The results of both direct comparison and cross comparison methods were presented in this study. The comparison results indicated that the quality of the HY-2B satellite’s geophysical data records (GDRs) data was excellent, with 95% of the sea surfaces effectively observed between 82 degrees north and south latitudes. In addition, the standard deviation of the sea level anomalies (SLA) at the single mission crossovers was 4.6 cm to 5.8 cm, and at the dual-crossovers with Jason-3, the standard deviation was determined to be 5.1 cm to 5.8 cm. The accuracy levels of the significant wave heights and products of the HY-2B satellite radar altimeter were observed to be greater than 0.3 m and 1.4 m/s (STD), respectively. Therefore, it was concluded in this study that the data quality and system performance of the HY-2B satellite were excellent and stable, and could be widely utilized in such fields as global sea-level change monitoring, wave numerical assimilation predictions etc.

Empirical algorithms have become the mainstream of significant wave height (SWH) retrieval from synthetic aperture radar (SAR). But the plentiful features from multi-polarizations make the selection of input for the empirical model a problem. Therefore, the XGBoost models are developed and evaluated for SWH retrieval from polarimetric Gaofen-3 wave mode imagettes using the SAR features of different polarization combinations, and then the importance of each feature on the models is further discussed. The results show that the reliability of SWH retrieval models is independently confirmed based on the collocations of the SAR-buoy and SAR-altimeter. Moreover, the combined-polarization models achieve better performance than single-polarizations. In addition, the importance of different features to the different polarization models for SWH inversion is not the same. For example, the normalized radar cross section (NRCS), cutoff wavelength (λ_c), and incident angle (θ) have more decisive contributions to the models than other features, while peak wavelength (λ_p) and the peak direction (φ) have almost no contribution. Besides, NRCS of cross-polarization has a more substantial effect, and the λ_c of hybrid polarization has a stronger one than other polarization models.

The co-located normalized radar backscatter cross section measurements from the Global Precipitation Measurement (GPM) Ku/Ka-band dual-frequency precipitation radar (DPR) and sea surface wind; wave and temperature observations from the National Data Buoy Center (NDBC) moored buoys are used to analyze the dependence and sensitivity of Ku- and Ka-band backscatter on surface conditions at low-incidence angles. Then the potential for inverting wind and wave parameters directly from low-incidence σ⁰ measurements is discussed. The results show that the KaPR σ⁰ is more sensitive to surface conditions than the KuPR σ⁰ overall. Nevertheless; both the KuPR σ⁰ and KaPR σ⁰ are strongly correlated with wind speed (U₁₀) and average wave steepness (δ_a) with the exception of specific transitional incidence angles. Moreover, U₁₀ and δ_a could be retrieved from pointwise σ⁰ near nadir and near 18°. Near 18°; wind direction information is needed as the effect of wind direction on σ⁰ becomes increasingly significant with incidence angle. To improve the performance of U₁₀ retrieval; especially for low U₁₀; auxiliary δ_a information would be most helpful; and sea surface temperature is better taken into account. Other wave parameters; such as significant wave height; wave period and wave age; are partly correlated with σ⁰. It is generally more difficult to retrieve those parameters directly from pointwise σ⁰. For the retrieval of those wave parameters; various auxiliary information is needed. Wind direction and wave direction cannot be retrieved from pointwise σ⁰.