Objective Aerosols play an important role in the formation of cloud and precipitation. Various lidar systems, distinguished by their high temporal and spatial resolution, are employed to investigate atmospheric properties. The raw data from traditional coherent Doppler wind lidar (CDWL) used in atmospheric detection is difficult to store due to its large volume, leading to the inversion of atmospheric parameters based on the coherently integrated power spectrum rather than raw data. However, the power spectrum loses information compared to the raw data. To make the sampled raw data easier to store, we need to reduce its size without significantly decreasing the CDWL performance. Methods We propose a one- bit sampling CDWL for atmospheric detection, reducing the resolution of the analog- to- digital converter (ADC) to the limit of one- bit, significantly reducing the size of sampling raw data volume and simplifying data storage. We employ comparators and a time- interleaved sampling structure to construct a one- bit sampling ADC with reduced computational complexity and power consumption. Results and Discussions The experimental results of continuous observations from both the 1 bit and 14 bit sampling channels are shown in Fig. 3. One- bit sampling is capable of detecting rapid changes in the atmospheric wind field and demonstrates excellent consistency in spectrum width and skewness, both aligning well with the simulated results. The differences in CNR and radial wind velocity are shown in Fig. 4. Influenced by turbulence, CNR exhibits significant enhancement, while radial wind velocity fluctuates. In the near field, the mean CNR difference is 3.28 dB with a standard deviation of 0.26 dB, whereas in the far field, it is 1.35 dB with a standard deviation of 0.78 dB. The one- bit sampling CDWL shows a slight CNR loss in the near field, but it does not affect atmospheric detection. The mean differences in radial wind velocity in the near and far fields are-0.08 m/s and-0.07 m/s, with standard deviations of 0.57 m/s and 0.86 m/s, respectively. Conclusions We propose a new one- bit sampling CDWL, demonstrating its advantages through simulation and comparison with a 14 bit sampling CDWL. This new one- bit sampling CDWL reduces the raw data volume to 1/16 and the power consumption of the sampling circuit to 2/9. Moreover, even under low CNR conditions, the returned signals can still be accurately distinguished. These findings reveal that the one- bit sampling can maintain the performance of CDWL, while effectively reducing the raw data volume, showcasing its potential for CDWL applications.