The Chinese HY-2C satellite was launched on Sep. 21, 2020, carrying the new Ku-band scatterometer (HSCAT-C). Different from the other currently operating scatterometers, the HSCAT-C is in a nonsun-synchronous orbit and as such it will be useful for the cross-calibration of sea surface wind products from current space-borne scatterometers and radiometers. In this article, the HSCAT-C wind products are validated by comparing to collocated winds from buoys, European Centre for Medium-Range Weather Forecasts model, and several other scatterometers. The results show that the quality of HSCAT-C winds is very good: in comparison with buoy winds, the wind speed standard deviation and direction root-mean-square error are 1.03 m/s and 15.9°, respectively. The HSCAT-C winds show very good agreements with the HSCAT-B winds, especially in the range of [4, 17] m/s. In addition, as HSCAT-C is a rotating pencil-beam scatterometer, some common error characteristics are also clearly found in the wind products. Based on the validation results and existing scatterometer application experience, it is believed that the availability of the HSCAT-C wind product would greatly benefit the scientific and user community.

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.

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.

This work is the first to analyse the sea surface wind vector (SSWV) data acquisition capabilities of eight satellites carrying microwave scatterometer (scanning scatterometer carried by Haiyang satellite 2A, advanced scatterometer carried by Metop satellite A, advanced scatterometer carried by Metop satellite B and scanning scatterometer carried by Oceansat satellite 2) or radiometers (Special Sensor Microwave Imager carried by Meteorological Satellite Program satellites F15 and F17, advanced microwave scanning radiometer 2 carried by GCOM-W1 satellite, and windsat polarimetric radiometer carried by Coriolis satellite) and investigate a SSWV fusion algorithm for active and passive remote-sensing data. We found that combining observations of the eight satellites can provide an SSWV data product with spatial resolution of 25 km × 25 km and temporal resolution of 3 h. Sea surface wind speed and direction data were obtained from multi-source active and passive sensors using a spatiotemporally weighted fusion algorithm. An adaptive sliding window was introduced for calculating effective observation data within spatial/temporal radii, which can effectively improve calculation efficiency for wind field fusion. Comparing the fused and buoy observation results, the root-mean-square errors of the wind direction and speed were 20.6° and 1.2 m s^–1, respectively, indicating that the fusion results can meet most application requirements for wind vector. Meanwhile, the space coverage, accuracy of merged wind speed and wind direction can be improved comparing to a single sensor.