###### Abstract  
Atmospheric wind is an essential parameter in the global observing system. In this study, the water vapor field in Typhoon Lekima and its surrounding areas simulated by the Weather Research and Forecasting (WRF) model is utilized to track the atmospheric motion wind through the Farneback Optical Flow (OF) algorithm. A series of experiments are conducted to investigate the influence of temporal and spatial resolutions on the errors of tracked winds. It is shown that the wind accuracy from tracking the specific humidity is higher than that from tracking the relative humidity. For fast-evolving weather systems such as typhoons, the shorter time step allows for more accurate wind retrievals, whereas for slow to moderate evolving weather conditions, the longer time step is needed for smaller retrieval errors. Compared to the traditional atmospheric motion vectors (AMVs) algorithm, the Farneback OF wind algorithm achieves a pixel-wise feature tracking and obtains a higher spatial resolution of wind field. It also works well under some special circumstances such as very low water vapor content or the region where the wind direction is parallel to the moisture gradient direction. This study has some significant implications for the configuration of satellite microwave sounding missions through their derived water vapor fields. The required temporal and spatial resolutions in the OF algorithm critically determine the satellite revisiting time and the field of view size. The brightness temperature (BT) simulated through Community Radiative Transfer Model (CRTM) is also used to track winds. It is shown that the error of tracking BT is generally larger than that of tracking water vapor. This increased error may result from the uncertainty in simulations of brightness temperatures at 183 GHz. F 2021 24 July 2021 27 July 2  
et al. [13] first artificially determined the movement of clouds on the visible images, which was inefficient and less accurate. [PERSON] et al. [14] used computer recognition technology to calculate the motion by calculating the brightness center according to the cloud area and brightness, which was similar to the gravity center in mechanics. [PERSON] et al. [15] determined the cross-correlation coefficients to obtain the wind field according to the principle of fast Fourier transform, and then many studies were made to improve this method [16; 17; 18]. AMVs are required to have a height information [19]. The uncertainty of height assignment has been proven to be one of the largest error sources that restricts the assimilation of AMVs into NWP [20]. To solve this problem, [PERSON] et al. [21] proposed using water vapor fields retrieved from satellite radiance data to track wind fields. Since the one-dimensional variation algorithm (1 DVAR) accurately assigns the water vapor height [22], calculating AMVs from these water vapor profiles can avoid the height assigned uncertainty. The quality of the retrieved water vapor profiles greatly influences AMVs' accuracy. Since visible or infrared instruments could not provide information under cloud conditions, the water vapor profiles retrieved from such wavebands can only be used to obtain the wind fields under clear sky conditions [21]. The microwave sounding from satellites can penetrate into clouds and precipitation conditions, and the data can be used to directly monitor the vertical structure of atmospheric temperature and water vapor of the active weather systems such as typhoons [12; 23]. The atmospheric 3D water vapor fields are retrieved from microwave sounders under different observation scenarios and possess spatial continuity and vertical consistency [24], making it possible to obtain all-sky 3D wind fields. However, the current microwave sounders are mainly carried onboard polar-orbiting satellites, and a constellation of these polar-orbiting satellites or a geostationary microwave sounding technology is required to obtain better wind field measurements [25; 26].