## 3 Results and Discussions
We do not have a clear explanation for that so far. Nevertheless, the \(\sigma^{0}\) values over the Amazon near sunrise are very close. The well-known NOC procedure was performed for HY-2B, HY-2C, and HY-2D measurements. In doing the NOC procedure, the simulated \(\sigma^{0}\) are computed via the NSCAT-4 GMF with the ECMWF model operational forecast winds (real 10-m winds) as inputs. Figure 9 shows the PDF of ECMWF wind speeds matched for HY-2B, HY-2C, or HY-2D. Noting that the NOC is performed based on wind vector cells (WVCs), instead of the original individual \(\sigma^{0}\) measurement contained in L1B product. The spatial size of a WVC is approximately \(25\times 25\) km\({}^{2}\), which is closer to the ECMWF model resolution than the original \(\sigma^{0}\) measurement. Besides, the normal quality control (QC) is applied to screen the data that may be contaminated by rain [28]. The PDFs of global wind speeds sampled by HY-2C and HY-2D are quite similar, while HY-2B shows noticeable differences from them. This is because the spatial coverage of HY-2C or HY-2D over a high latitude area is more frequent than HY-2B. Nevertheless, the differences in wind speed PDF have a limited effect on the NOC results. Figure 7: The results of nonlinear calibration obtained using the CDF matching technique. It is masked if the number of samples in a bin (size of 0.1 dB) is less than 1000. (**a**) HH measurements, (**b**) VV measurements. In summary, the results among the three methods are comparable and their differences are roughly within 0.1 dB. The relative calibration values provided by the three methods are all small, which means the initial corrections (introduced in Section 2.1) for individual instruments are quite consistent. To validate the appropriateness of all calibration results of the instruments, we compare the scatterometer winds with ECMWF model winds. Figure 10 shows the wind speed bias between HY-2 scatterometer and ECMWF model winds as a function of average wind speed. In the scatterometer wind retrieval processing, the same procedure (including NOC calibration, QC, GMF, and NWP auxiliary data) is followed for HY-2B, HY-2C, and HY-2D. The wind speed PDFs of scatterometer and ECMWF winds are shown in Figure 9.  
As shown in Figure 10, the sea surface winds are very consistent among HY-2B, HY-2C, and HY-2D for wind speeds above 4 m/s and below 20 m/s, particularly between HY-2C and HY-2D. This is essentially in line with the results provided by the collocation method (shown in Figure 7). HY-2 scatterometer wind speeds are lower than ECMWF winds, and the biases increase as wind speed increases over the range from 8 to 18 m/s. In line with earlier studies [9; 10; 11; 12], we conclude that this pattern is mainly caused by the NSCAT-4 GMF, the ECMWF wind input, and partly by the NOC results. Besides, the highest speed bins suffer from statistical sampling artefacts [29]. In particular, at high wind speed (>12 and <22 m/s) conditions, the wind speed biases compared with ECMWF winds among HY-2 scatterometers are all negative, since many high winds occur over cold sea surface temperatures, where the GMF produces too high a roughness at a provided input ECMWF wind speed. Conversely, backscatter values provide too low wind speeds after inversion over cold SST and hence the negative bias. Second, the ocean roughness responds to the local air mass density [21], which was not taken into account here and to atmospheric stability, which reduces the global wind by about 0.2 m/s, corresponding to approximately 0.2 dB [30]. Thirdly, different QC classes correspond to different backscatter biases with respect to the GMF, which is caused by the probability of the detection of rain  
Figure 8: Hourly average \(\sigma^{0}\) over the Amazon as measured by HY-2B, HY-2C, and HY-2D in summer, 2021. Marker “ stands for HH polarization, and “ stands for VV polarization. and by the retrieval validation in enhanced wind variability conditions (without rain) [31].