In the study of atmospheric wind fields from the upper troposphere to the stratosphere (10 km to 50 km), direct detection wind LiDAR is considered a promising method that offers high-precision atmospheric wind field data. In 2020, Xie et al. of the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, developed an innovative rotating Rayleigh Doppler wind LiDAR (RRDWL). The system aims to achieve single-LiDAR detection of atmospheric wind fields by rotating the entire device cabin. In 2022, the feasibility of the system was successfully validated in laboratory conditions, and field deployment was completed. Due to the structural differences between this system and traditional direct-detection wind LiDAR, performance tests were conducted to evaluate its continuous detection capability in outdoor environments. Subsequently, based on the test results and error analysis, further analysis was carried out to identify the main factors affecting the system’s detection performance. Finally, the error analysis and traceability of the detection results were conducted, and corresponding measures were discussed to provide a theoretical foundation for optimizing the performance of RRDWL.

Due to the complex and variable nature of the atmospheric conditions, traditional multi-wavelength differential absorption lidar (DIAL) methods often suffer from significant errors when inverting ozone concentrations. As the detection range increases, there is a higher demand for Signal to Noise Ratio (SNR) in lidar signals. Based on this, the paper discusses the impact of different atmospheric factors on the accuracy of ozone concentration inversion. It also compares the advantages and disadvantages of the two-wavelength differential method and the three-wavelength dual-differential method under both noisy and noise-free conditions. Firstly, the errors caused by air molecular extinction, aerosol extinction, and backscatter terms in the inversion using the two-wavelength differential method were simulated. Secondly, the corrected inversion errors were obtained through direct correction and the introduction of a three-wavelength dual differential correction. Finally, addressing the issue of insufficient SNR in practical inversions, the inversion errors of the two correction methods were simulated by constructing lidar parameters and incorporating appropriate noise. The results indicate that the traditional two-wavelength differential algorithm is significantly affected by aerosols, making it more sensitive to aerosol concentration and structural changes. On the other hand, the three-wavelength dual differential algorithm requires a higher SNR in lidar signals. Therefore, we propose a novel strategy for inverting atmospheric ozone concentration, which prioritizes the use of the three-wavelength dual-differential method in regions with high SNR and high aerosol concentration. Conversely, the direct correction method utilizing the two-wavelength differential approach is used. This approach holds the potential for high-precision ozone concentration profile inversion under different atmospheric conditions.