# Blending Sea Surface Winds from the HY-2 Satellite Scatterometers Based on a 2D-Var Method
## 5 Conclusions  
This paper examines the feasibility of developing a six-hourly global sea surface wind product based on the scatterometers onboard the HY-2 satellite constellation. Benefiting from the orbit configuration of HY-2 satellites, i.e., HY-2B operated in a sun-synchronous orbit and HY-2C & -D operated in an inclined orbit; the three HSCATs together are able to observe more than 70% of sea surface area within six hours, providing unprecedented temporal coverage in the perspective of remote sensing. Moreover, the HSCATs vector winds are verified using both buoy measurements and ERA5 background fields. The results show that the winds derived from the three HSCATs are consistent with each other, and they are in good agreement with both ERA5 and buoy winds. Consequently, it does not need a bias correction for the wind blending procedure. A 2D-Var approach, which is adapted from the ambiguity removal procedure for spaceborne scatterometers, is proposed to blend HSCATs winds with the ERA5 background fields. Two relevant aspects of the 2D-Var cost function are considered in this study. On the one hand, the observation error is estimated as a function of the time difference between **O** and **B**, and then a time-dependent observation-to-background error ratio is implemented in the 2D-Var procedure. On the other hand, two different background error correlation functions, namely GSF and EBEC, are tested in order to look for an appropriate 2D-Var setting for wind blending. A comprehensive verification against the buoy winds  
Figure 7: The normalized histograms of wind \(u\) (**a**) and \(v\) (**b**) differences for ERA5 versus buoys (black), GSF-derived results versus buoys (red), and EBEC derived results versus buoys (blue), respectively. (**c,d**) are the same as the upper panels separately, but using the HSCAT winds as reference. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistical Scores** & **Speed (m/s)** & **Direction (\({}^{\circ}\))** & \(u\) (m/s)** & \(v\) (m/s) \\ \hline \multirow{3}{*}{GSF} & Obs & 1.20 & 15.6 & 1.61 & 1.89 \\  & No Obs & 1.77 & 19.3 & 2.16 & 2.31 \\  & Overall & 1.51 & 17.5 & 1.90 & 2.11 \\ \hline \multirow{3}{*}{EBEC} & Obs & 1.17 & 15.8 & 1.62 & 1.89 \\  & No Obs & 1.60 & 18.0 & 1.99 & 2.15 \\  & Overall & 1.40 & 16.9 & 1.81 & 2.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SD scores of the blended winds with regards to. buoy measurements. demonstrates that the use of EBEC generally leads to a better-blended wind product than the GSF, probably due to the larger length scales used for EBEC than for GSF. As such, the used EBEC adds more small-scale variance than GSF to the blended winds. Furthermore, note that the quality of blended wind data degrades remarkably in case where there is no HSCATs observation presented (see Table 5). Such cases occur over \(\sim\)30% of the sea surface. Consequently, more scatterometer observations are demanded to fill the HSCATs gaps in order to further improve the blended wind quality. Future research activities will include the blending of HSCATs winds with ASCAT wind data or the radiometer wind speed data. Conceptualization W.L. and J.Z. ; methodology, S.L. and Z.W. ; software W.L. and S.L. ; validation and formal analysis, S.L. and Z.W. ; writing, review and editing, S.L., W.L., Z.W. and J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China under Grant 2022 YFC3104900 and Grant 2022 YFC3104902. Not applicable. The authors would like to acknowledge NSOAS for providing straightforward and rapid access to the HY-2B data. We would also like to acknowledge the Climate Data Store of the Copernicus Programme for providing the ERA5 data. The 2D-Var software is adapted from the ambiguity removal module developed by the Royal Netherlands Meteorological Institute (KNMI) scatterometer team in the context of EUMETSAT Satellite Application Facility for Numerical Weather Prediction.