## 3 Results and Discussions
Note in particular that enhanced wind variability near moist convection is not resolved by the ECMWF model [32] and the NOC in tropical rainy areas will therefore be offset. Finally, we note that both persistent small-scale and large-scale biases exist in the ECMWF model [32], such that satellites with different sampling may collect different NOC values. This latter effect appears small for the sampling difference between HY-2B and both HY-2C and HY-2D, since the NOC intercalibration effects are generally small for VV (Table 2). However, Table 2 shows that the backscatter intercalibration in NOC for the HH signal can reach 0.2 dB, which is not negligible. The collocation method appears the most direct method of intercalibration, as it uses an identical weather sample for two instruments. For the rainforest and NOC method, different samples are chosen, but rather a fixed procedure is used. The rainforest method uses a relatively small domain with variable sampling and backscatter variations in time and location. NOC uses the global oceans as target and prior information on its variability, which is well forecast by ECMWF generally. However, we note that GMF errors and ECMWF model changes will affect NOC. Rainforest variability and errors related to NOC may cause uncertainty in the results. Using HY-2B as reference and the collocation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Collocated**} & \multicolumn{2}{c}{**Rainforest**} & \multicolumn{2}{c}{**NOC**} \\ \cline{2-7}  & **HH (dB)** & **VV (dB)** & **HH (dB)** & **VV (dB)** & **HH (dB)** & **VV (dB)** \\ \hline HY-2B and HY-2C & +0.026 & 0.114 & \(-\)0.063 & \(-\)0.002 & +0.027 & +0.076 \\ HY-2B and HY-2D & \(-\)0.027 & +0.006 & \(-\)0.054 & +0.026 & +0.020 & +0.076 \\ HY-2C and HY-2D & 0.000 & 0.000 & +0.010 & +0.029 & \(-\)0.007 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of backscatter intercalibration among HY-2 series scatterometers after small dB corrections to HY-2C and HY-2D VV and HH (see text). Figure 9: Wind speed PDFs of HY-2 series scatterometer winds and the collocated ECMWF model winds. differences as leading, one can compute the corrections for VV and HH for HY-2C as the sum of half the differences of HY-2B-HY-2C, HY-2B-HY-2D and HY-2D-HY-2C, resulting in \(-0.054\) and \(+0.106\) dB corrections, respectively. Similarly, for VV and HH of HY-2D, the sum of half the differences of HY-2B-HY-2C, HY-2B-HY-2D and HY-2C-HY-2D, result in \(+0.013\) and \(+0.182\) dB corrections, respectively. By applying these corrections for HY-2C and HY-2D, we obtain Table 3 based on Table 2.  
One may note from Table 3 that the collocation differences with respect to HY-2B are within a few hundredths of a dB, similar to the relative accuracy of ASCAT and ERS calibration in measurement space [16]. The uncertainty in the rainforest calibration appears generally larger, in particular for HH against HY-2B. The different sampling of the scatterometers appears to introduce the largest uncertainty for the NOC method, particularly for VV. Nevertheless, in this representation all differences are well-below 0.1 dB for any method. Differences between HY-2C and HY-2D are particularly small between the different methods. The small remaining uncertainty in the VV and HH calibration can potentially cause effects in the wind direction retrieval and in the mutual consistency of the measurements with the GMF. The latter is expressed in the wind retrieval residual and quantified as a maximum likelihood estimate (MLE), which is also used to detect geophysical anomalies for wind retrieval, such as rain [26]. Hence, small calibration changes in VV and HH, or VV with respect to HH, may affect QC, rain contamination, GMF definition, MLE values and wind speed and direction. These effects can be further investigated for all scatterometers after intercalibration.