## 3 Results and Discussion
### Error Comparison by Using Water Vapor and Brightness Temperature Data  
To confirm that our OF wind-tracking algorithm is suitable for the wind retrieval from MW instruments, we track the simulated MWHS brightness temperature to retrieve winds. We calculate the wind speed RE (Figure 12(a)), wind speed RMSE (Figure 12(b)), and the wind direction AE (Figure 12(c)) of the five layers listed in Table 2. The errors based on BT and water vapor profiles are shown as dash and solid lines, respectively, in Figure 12, with different colors indicating varying vertical heights. The general tendency of two data errors is basically the same. With the time interval flowing, the wind speed RE changes very gently. The wind speed RMSE increases slowly and the wind direction AE decreases slowly over time. The wind speed and wind direction errors from tracking BT and water vapor have the same tendency and distribution with heights. For instance, the largest wind speed error of BT and water vapor all occur at 600 hPa, and the least error is present at 800 hPa. While the OF wind-tracking algorithm can be used for microwave sounding instruments, the wind speed and wind direction errors of using BT are larger than that of using water vapor data. The uncertainty in the height assignment of BT data could be part of error sources. Since BT at 183 GHz is affected by atmospheric variables such as water vapor and temperature profiles, its change with time is a direct reflection of water vapor movement in the atmosphere.  
In addition, BT simulated by the radiative transfer model also suffers from the systematic errors in atmosphere gaseous absorption, cloud and precipitation scattering, and emission. [PERSON] et al. (2016) discussed the systematic errors and uncertainties between 183 GHz simulations through radiative transfer model and spaceborne measurements. The channel dependent errors located in the wings of line are nearly 3 K larger than those in the center of the line. Brightness temperatures at the wings of the 183.31 GHz water vapor absorption band are mainly sensitive to the lower tropospheric humidity. Therefore, the systematic errors are larger in the lower troposphere As for the water vapor data simulated by WRF model; we also ignored the turbulence in the microwave instrument FOV. [PERSON] et al. (2017) compared the radiative transfer modeling errors at 183 GHz of the inhomogeneous and uniform FOVs. The results show that the turbulent nature of water vapor is a critical factor in simulations, and the biases are also frequency-dependent. The turbulence in the upper troposphere, storm, or cumulus clouds can cause large water vapor inhomogeneity and result in large errors in the upper atmosphere. The larger inhomogeneity of water vapor retrieved from microwave sounding instruments may be purely a result of the scan-angle dependent FOV. Meanwhile, the different weather conditions from clear to cloud and rain conditions can influence the accuracy of water vapor retrieval, especially in the typhoon area ([PERSON] et al., 2018). Since the geostationary microwave observation data are not available yet, we choose to use the simulated BT data and WRF water vapor data for our current experiments.  
Figure 12: Wind speed RE at different heights and time intervals by using relative humidity (**a**) and specific humidity (**b**) is a figure.