## 4 Conclusions  
This study explores the potential of the satellite microwave sounding data for tracking the atmospheric winds. The Farenback OF wind-tracking algorithm is developed to obtain an all-sky 3D wind field of Typhoon Lekima and its surrounding areas. The sensitivity of wind-tracking errors is expressed as a function of satellite revisiting time and FOV sizes. The relationships between wind retrieval error and water vapor content, water vapor gradient, wind speed, and wind-moisture gradient angles are analyzed as well. Our conclusions are summarized as follows:  
1. Specific humidity is suggested to be used as a water vapor tracer to obtain wind field, especially under convective areas. Compared with relative humidity, specific humidity is less affected by temperature changes and more accurately characterizes water vapor changes and movement. Tracking specific humidity features can achieve a more accurate wind field. 2. The wind retrieval error decreases as the FOV size decreases, and the error sensitivity to the satellite revisiting time gradually increases. Therefore, if the sensor's spatial resolution is extremely poor, shortening the revisiting time will not improve the wind field results. 3. For fast-evolving weather systems such as typhoons, the Farneback OF wind-tracking algorithm requires a very fine satellite revisiting time. In the central area of the typhoon, due to the fast-moving water vapor field with vertical convergence and divergence, the error is reduced with shorter temporal-spatial resolution. 4. For the non-typhoon areas where the water vapor movement is relatively stable, the water vapor field with a time interval of at least 30 min is tracked for conducting a more accurate wind field. The difference between images is too subtle to be identified  
Figure 13: Wind speed RE (**a**), wind speed RMSE (**b**), and wind direction AE (**c**) at different heights and time intervals by using water vapor and brightness temperature. at a short revisiting time. The algorithm noise will seriously interfere with the signal, which leads to a large proportion of the signal and large retrieval errors. * The Farneback OF wind-tracking algorithm can realize pixel-wise tracking. It can still obtain an accurate wind field when water vapor content is very low or the wind direction is parallel to the moisture gradient. Compared with the traditional wind-tracking method, our algorithm is more accurate for broader applications. * The error of tracking simulated BT is larger than that of tracking WRF water vapor fields. Height assignment uncertainty, inclusion of temperature fields, and the systematic errors of BT simulated by radiative transfer model may increase the errors. After the geostationary microwave sounder data are available, the retrievals will consider accuracy, the uncertainty in water valor inhomogeneity, scan angle, and various weather events so that a high quality of water vapor field can be derived for wind tracking. As [PERSON] (2003) pointed out, the main disadvantage of the algorithm is its assumption of the slowly changing displacement field, resulting in image discontinuities being smoothed. Thus, a certain error still exists in a relatively flat water vapor area with a short revisiting time. Future work will consider combining some image segmentation methods to amplify the small displacements in these areas to reduce the noise proportion and improve the accuracy. The current microwave measurements mainly rely on polar-orbiting satellites of which the time resolution cannot meet the demand for wind tracking. However, the Farneback OF wind-tracking algorithm in this study is ready for the applications of future geostationary microwave sounders and a constellation of more small satellites cubesats to obtain more accurate atmospheric 3D wind fields. Conceptualization, F.W. and H.H. ; writing--original draft preparation, Y.Z. All authors have read and agreed to the published version of the manuscript.