## 1 Introduction  
The study of atmospheric wind fields has long been a key focus in meteorology and aerodynamics. The investigation of the underlying principles governing the evolution of wind fields has been a pivotal objective in this field of research. In this regard, the CDWL has emerged as a prominent tool for atmospheric wind field research. In comparison to traditional wind speed detection equipment, the CDWL exhibits several advantageous characteristics, including a compact design, high temporal and spatial resolution, and rapid response time [1, 2, 3, 4]. These attributes have contributed to its extensive utilization in diverse fields, including wind power generation, civil aviation safety, and meteorological monitoring.  
To enhance the CDWL detection precision, scholars have conducted extensive research. [PERSON] et al. [5] proposed a CNN-based radar echo de-noising algorithm to reduce the noise impact on the accuracy. [PERSON] et al. [6] proposed a weighted sine-fitting method that considers both the signal strength and spatial continuity of wind to mitigate the impact of poor radial wind speed accumulation on the wind speed estimation accuracy. [PERSON] et al. [7] combined a detector nonlinear effect and Optical Local-Oscillator Power (OLP) to find the optimal OLP for better accuracy and range. [PERSON] et al. [8] proposed a variable range gate division method with distance in a comparative study of two algorithms to improve the long-range detection accuracy. However, fixed-range gates face challenges in complete cycle truncation of echo signals during wind speed measurement, causing spectral leakage and errors in frequency estimation. [PERSON] et al. [9] proposed an adaptive range gate division method based on whole-period searching to reduce the spectral leakage impact, but it has a long data processing time and poor real-time performance. [PERSON] et al. [10] added a Hanning window to suppress the spectral leakage with 0.3 m/s accuracy. [PERSON] et al. [11] proposed a spectral refinement and interpolation-based algorithm to significantly improve the signal processing accuracy. [PERSON] et al. [12] proposed a second-order self-convolution window with a five-term MSD algorithm to enhance the detection accuracy. [PERSON] et al. [13] proposed a frequency-shifting algorithm for Doppler sampling to reduce the specific leakage and showed that it outperforms the Hamming window through experiments.  
Although the use of adaptive-range gate dividing and classical window functions can alleviate spectral leakage, the former has poor real-time performance, while it is difficult for the latter to balance the main lobe width and sidelobe attenuation. In response to the above issues, this article proposes using the HSCW to preprocess the echo signal, aiming to reduce the impact of spectral leakage on the wind speed measurement. Using the Hanning window as the parent window with a proper convolution order can minimize the main lobe width and improve the spectral inversion accuracy while keeping good sidelobe decay [14,15]. Compared with adaptive-dividing range gates, adding HSCW for data preprocessing does not require extracting features from the time-domain signals of wind field echoes, reducing the delay caused by complex algorithms and improving the temporal resolution of coherent Doppler wind lidar detection. The article validates the proposed method's efficacy and practicality through simulations and outdoor wind speed comparison experiments. This study is significant for CDWL technology development, facilitating more accurate wind field inversion.