## 1 Introduction  
Wind power has become one of the most promising and cost-effective powers in the new energy industry with increasingly mature wind power generation techniques and a reduction of wind turbine manufacturing costs [1, 2]. With the increase in wind turbine size, the hub height is increasing from 40 m to more than 100 m and the diameter of turbine blades has approached 200 m with an influence height reaching 200-300 m [3, 4]. The continuous increase in the blade size has led to an increasing demand for wind profile information reaching 300-400 m from the ground and the increasing wind farm size has also attracted increasing attention to its impact on local and even regional wind regimes. For example, a large number of observations and numerical simulation studies have shown that wind farm operation significantly reduces wind speed in downstream areas [4], especially in the blade sweeping area [5]. Currently, wind energy assessments still rely on observations from wind towers. Due to construction costs, the observation range of most wind towers is limited to about 100 m [6]. To obtain wind profiles above 100 m, numerical simulations or power law extrapolations are often used [7]. But these methods are still not accurate enough compared to in situ observations [8], due to the lack of time-dependent and spatially explicit power law exponent estimations or unresolved properties, e.g., complex terrain. In recent years, Doppler Light Detection and Ranging (Lidar), due to its advantages of high-detection accuracy and high-temporal resolution, is replacing costly wind towers and being applied in wind resource assessment and wind profile characteristics research [9; 10]. The vertical detection range of Doppler Lidar ranges from tens to hundreds of meters and can reach over 1 km under good weather conditions [11]. However, there is a blind area between the height of the instrument and the lowest reliable range gate (about tens to hundreds of meters) due to the issue of incomplete optical overlap in the near range [12; 13]. This hinders the detection of the complete wind profile extending from the near-ground (few meters above the ground) to the blade-affecting area. Scanning Doppler Lidar can overcome the limitations of vertical detection blind area by scanning at low-elevation angles [14]. Since Doppler Lidar can only measure the wind speed along the emitted beam, i.e., line-of-sight or radial wind speed, researchers have proposed various vertical wind profile inversion methods for horizontally homogeneous or linear wind fields, based on, e.g., velocity-azimuth display (VAD), volume-velocity processing (VVP) and Doppler beam-swinging (DBS) scanning [15; 16; 17; 18]. Different variants of these methods have been successfully applied in atmospheric boundary layer wind profiling studies. For example, [PERSON] et al. [19] validated the accuracy of Lidar-derived wind speed within 100 m by comparing with observations from a meteorological tower; [PERSON] et al. [20] demonstrated that Doppler Lidar can be used for wind speed monitoring by examining the consistency between the results of Doppler Lidar, wind profiling radar, and sounding; [PERSON] et al. [21] quantified the errors caused by turbine wake on DBS scanning; [PERSON] et al. [22] validated the accuracy and reliability of Lidar and SODAR measurements in complex terrain wind farms by comparing them with traditional meteorological tower measurements. Although there are many relevant studies, most of them have not provided details on Lidar data quality control and validation methods, and the applicability of scanning Lidar for obtaining wind profiles within wind farms is still unclear. The purpose of this study is to use a single scanning Doppler Lidar with the velocity-azimuth display (VAD) scanning strategy to derive the whole vertical wind profiles from the near ground to the height affected by the wind turbine blades.