## 3 Results and Discussion  
### AMVs Sensitivity to FOV Sizes at Various Heights  
Here, we select four layers of 850, 500, 300, and 100 hPa for analysis as the experiment results indicate that AMVs sensitivity to the FOV sizes has obvious vertical characteristics. The WRF wind fields of 850, 500, 300, and 100 hPa are shown as Figure 5a,c,e,g, respectively. The OF wind fields of the selected four layers are shown as Figure 5b,d,f,h, respectively. The initial tracking time is 12:00 on 8 August 2019 (UTC), and the satellite revisiting time is set as 5 min.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quality Control Code** & **Definition** \\ \hline
0 & Good quality \\
1 & Exceeding the research area \\
2 & Water vapor loss or abnormal \\
3 & U component accelerated velocity over 10 m/s\({}^{2}\) \\
4 & V component accelerated velocity over 10 m/s\({}^{2}\) \\
5 & Both satisfied with 3 and 4 \\
6 & Wind speed lower than 0.5 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The quality control strategy.  
Figure 5: _Cont._To explore the relationship between the error and FOV sizes, we analyze the error of different layers with various FOV sizes and revisiting time. On every layer, the spatial resolution is adjusted from 3 to 6, 12, 18, 24, and 30 km. The resolution degradation method has been described in Section 2.1. We track wind of the data with a different spatial resolution (different FOV sizes) on every layer. Figure 6a,c,e,g show the wind speed relative error (RE) at 850, 500, 300, and 100 hPa, respectively. The wind speed RE is computed as  
\[\mathrm{RE}=\left[\sum_{i=1}^{N}\left(\frac{\left|\sqrt{(u_{\mathrm{WRF}}^{2}+v _{\mathrm{WRF}}^{2})}-\sqrt{(u_{\mathrm{OF}}^{2}+v_{\mathrm{OF}}^{2})}\right|}{ \sqrt{(u_{\mathrm{WRF}}^{2}+v_{\mathrm{WRF}}^{2})}}\right)/N\right]\times 100\% \tag{2}\]  
where the \(u_{\mathrm{WRF}}/v_{\mathrm{WRF}}\) represents the WRF wind, \(u_{\mathrm{OF}}/v_{\mathrm{OF}}\) represents the OF wind, and the \(N\) is the number of data points. Figure 6b,d,f,h show the root mean square error (RMSE) at four levels. The wind speed RMSE is computed as:  
\[\mathrm{RMSE}=\left\{\sum_{i=1}^{N}\left[\left(\sqrt{(u_{\mathrm{WRF}}^{2}+v _{\mathrm{WRF}}^{2})}-\sqrt{(u_{\mathrm{OF}}^{2}+v_{\mathrm{OF}}^{2})}\right) ^{2}\right]/N\right\}^{1/2} \tag{3}\]  
Figure 5: The WRF wind fields and OF wind fields of 850 (**a,b**), 500 (**c,d**), 300 (**e,f**), and 100 hPa (**g,h**) on 12:05 on 8 August 2019 (UTC). The satellite revisiting time is set to 5 min. FOV size is set to 3 km.  
Figure 6: The relative error (**a,c**,**c**,**e**,**g**) and the root mean square error (**b,d,f,h**) with different FOV sizes (shown as lines of different colors): 3, 6, 12, 18, 24, and 30 km. The X-axis represents satellite revisiting time ranging from 5 to 120 min in 5 min intervals.  
The X-axis represents the satellite revisiting time ranging from 5 to 120 min, and the solid lines in different colors represent different FOV sizes. Results show that the error continues to reduce as the FOV sizes decrease, indicating that the error is sensitive to FOV sizes. We can conclude that increasing the satellite spatial resolution leads to higher accuracy wind fields. In addition, changing the spatial resolution will also affect the AMVs sensitivity to revisiting time. AMVs present more sensitivity to the revisiting time at small FOV sizes (Figure 6a,b,d,h). The RE and RMSE are irrelevant to the satellite revisiting time at large FOV sizes (18-30 km). It indicates that for the satellite data with low spatial resolution, it is hard to improve the wind-tracking accuracy even if the satellite revisiting time is shortened.