The Chinese new marine dynamic environment satellite HY-2D was launched on 19 May 2021, carrying a Ku-band scatterometer (named HSCAT-D). In this study, wind products observed by the HSCAT-D were validated by comparing with wind data from the U.S. National Data Buoy Center (NDBC) buoys and European Centre for Medium-Range Weather Forecasts (ECMWF) model. The statistical results show that the HSCAT-D winds have a good agreement with the buoys’ wind measurements: in comparison with buoy winds, the wind speed standard deviation (STD) and root-mean-squared errors (RMSE) of direction were 0.78 m/s and 14.10°, respectively. Other scatterometers’ wind data are also employed for comparisons, including the HY-2B scatterometer (HSCAT-B), HY-2C scatterometer (HSCAT-C), and MetOp-B scatterometer (ASCAT-B) winds. The statistical results indicate that errors for HSCAT-D winds are smaller than HSCAT-C but a little bit larger than HSCAT-B. The spectral analysis shows that the HSCAT-D wind products contain less small-scale information than ASCAT-B. Moreover, the Extended Triple Collocation (ETC) results show that the HSCAT-D wind product is of good quality and well-calibrated. We believe that the HSCAT-D wind products will be helpful for the scientific community, as shown by the encouraging validation results.

Scatterometer wind data is critical for meteorological and oceanographic applications. The differences between scatterometer winds and buoy reference winds largely depend on the type of reference wind used in the fitting of the scatterometer’s Geophysical Model Function (GMF). This study evaluates scatterometer winds using advanced buoy reference winds, specifically stress-equivalent winds (U10S) and equivalent-neutral winds (U10N). We utilized HSCAT-B scatterometer wind products retrieved using both NSCAT-4 and NSCAT-5 Geophysical Model Functions (GMFs). NSCAT-4 winds were corrected to stress-equivalent winds, and these scatterometer winds were compared with various buoy reference winds, including true buoy winds, stress-equivalent winds, and equivalent-neutral winds. The results show that in extratropical regions, stress-equivalent winds provided a closer match to scatterometer winds, while in tropical regions, equivalent-neutral winds exhibited smaller errors. Scatterometer winds derived from the NSCAT-5 GMF demonstrated superior consistency with buoy reference winds, further reducing wind speed biases compared to NSCAT-4. An Extended Triple Collocation (ETC) analysis was conducted to address the uncertainties arising from differences in spatial resolution between scatterometer and buoy measurements. The findings emphasize the importance of using appropriate wind correction methods for scatterometer wind validation, particularly in regions with significant atmospheric variability.

Satellite altimeters provide global observations of significant wave height (SWH, in m), yet buoy-based validation is affected by representativeness errors and sampling mismatches. This study develops a consistent framework for validating and scaling HY-2B SWH that integrates nearest-point spatiotemporal collocation, sea-state-binned diagnostics, three complementary calibration schemes (bias correction, ordinary least-squares (OLS) linear regression scaling, and machine-learning residual correction), and Extended Triple Collocation (ETC) for sensor-independent uncertainty estimates. The dataset includes HY-2B SWH, National Data Buoy Center (NDBC) buoy records, seven buoys in the Taiwan Strait, and the sea surface significant wave height (VHM0, in m) from the Copernicus Marine Environment Monitoring Service (CMEMS) Global Wave Reanalysis. Sensitivity tests show that tightening the collocation radius from 100 to 25 km reduces scatter (RMSE/STD) while preserving near-zero bias; correlations remain ≥0.97 for 25–50 km but degrade at larger windows, underscoring representativeness effects. Error metrics increase monotonically with sea state, whereas mean biases remain small. ETC applied to HY-2B, NDBC, and CMEMS yields random error standard deviations of 0.158, 0.147, and 0.179 m, respectively, with squared correlation coefficients (${\rho }^2$) of approximately $0.96$–$0.98$ for all systems. Scaling experiments reveal a data-quality-dependent behavior: for NDBC matchups, HY-2B already agrees closely with buoys (e.g., RMSE ≈ 0.24 m), and additional scaling brings no benefit; for the Taiwan Strait buoys, all three schemes improve agreement (RMSE ≈ 0.41 m; correlation ≈ 0.95), with the residual machine-learning model providing the largest reduction in random error. The results support a practical protocol for HY-2B SWH validation: a 30 min/25–50 km window, modest outlier screening, and selective use of linear or residual corrections depending on buoy network and environment.