Since the mobile Rayleigh Doppler lidar of the University of Science and Technology of China was developed in 2013, more than 100 days of valid nighttime wind data from 15 to 60 km altitude were obtained during recent 3 years. The observation locations cover the northwest (midlatitude) of China: Delingha (37.371°N, 97.374°E), Xinzhou (38.425°N, 112.729°E), and Jiuquan (39.741°N, 98.495°E). Recently, we have extracted perturbations of the wind profiles from the wind field measurements and we have found that inertia gravity waves and mountain waves existed at the same time. The results of wind field and several gravity waves cases are shown in this paper. Typical characteristics of the gravity waves are analyzed in this midlatitude area of China. A 2‐D fast Fourier transform of the wind perturbation shows that a dominant stationary wave mode and a downward wave mode exist simultaneously in the spectrum. A band‐pass 2‐D filter was applied to the spectrum followed by inverse fast Fourier transform to separate inertia gravity waves from stationary mountain waves. The horizontal wavelength is retrieved using hodograph methods, indicating that the inertia waves are generated thousands of kilometers away. Observed mountain waves from a combination of vertical wind and leaned line of sight wind measurements show a small‐angle leaned wave front from the horizontal direction. This kind of gravity wave observation of the stratospheric wind field and its wave patterns is rare and significant for the study of atmospheric dynamics.

The coherent Doppler wind lidar (CDWL) has long been thought to be the most suitable technique for wind remote sensing in the atmospheric boundary layer (ABL) due to its compact size, robust performance, and low-cost properties. However, as the coherent lidar exploits the Mie scattering from aerosol particles, the signal intensity received by the lidar is highly affected by the concentration of aerosols. Unlike air molecules, the concentration of aerosol varies greatly with time and weather, and decreases dramatically with altitude. As a result, the performance of the coherent lidar fluctuates greatly with time, and the detection range is mostly confined within the planetary boundary layer. The original data collected by the lidar are first transformed into a spectrogram and then processed into radial wind velocities utilizing algorithms such as a spectral centroid. When the signal-to-noise ratio (SNR) is low, these classic algorithms fail to retrieve the wind speed stably. In this work, a radial wind velocity retrieving algorithm based on a trained convolutional neural network (CNN) U-Net is proposed for denoising and an accurate estimate of the Doppler shift in a low-SNR regime. The advantage of the CNN is first discussed qualitatively and then proved by means of a numerical simulation. Simulated spectrum data are used for U-Net training and testing, which show that the U-Net is not only more accurate than the spectral centroid but also achieves a further detection range. Finally, joint observation data from the lidar and radiosonde show excellent agreement, demonstrating that the U-Net-based retrieving algorithm has superior performance over the traditional spectral centroid method both in accuracy and detection range.

Since the first spaceborne Doppler wind lidar, Aeolus, was launched, global wind field observations from space have been possible. Several ground‐ and air‐based validations followed, although most of these comparisons remained below 10 km in the troposphere, with rare validation work for the stratosphere. The Rayleigh Doppler lidar developed by the University of Science and Technology of China (USTC) was deployed in Xinjiang, China, in 2017. It can observe wind speed and temperature in the stratosphere and mesosphere. By setting two geographical ranges centered on the USTC lidar, the Aeolus Rayleigh winds within these ranges can be compared with ground‐based lidar wind data. Furthermore, after eliminating the effect of particulate backscatter on the USTC lidar, the lower limit of the detection range was extended to 10 km to obtain more samples. The mean biases between the Aeolus winds and the USTC lidar winds were 1.05 ± 5.98 (−0.35 ± 4.78) m/s, 1.80 ± 6.30 (−1.88 ± 4.97) m/s, and 0.17 ± 5.45 (0.51 ± 4.44) m/s for all data, ascending orbits, and descending orbits, respectively, in a large (small) geographical range. The geographical distance between the USTC lidar and the Aeolus observation swaths has a significant effect on the wind bias, the farther the distance between Aeolus observation swaths and the USTC lidar, the greater the bias. Whereas, the wind biases have no significant dependency on the time difference of the USTC lidar observation to Aeolus overflight time. Overall, the Aeolus winds are consistent with the USTC lidar winds in the stratosphere.

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results.

Stratospheric airship navigation requires accurate wind field measurements at a ~20 km altitude, where low pressure and density limit the effectiveness of conventional wind sensors. To address this, we present a compact direct-detection Rayleigh Doppler wind lidar based on the molecular double-edge technique. The system utilizes a 532 nm fiber-coupled pulsed laser (0.5 W, 5 ns) and a fixed-cavity dual-channel Fabry–Perot etalon as the frequency discriminator. A liquid crystal variable retarder (LCVR) combined with a polarization beam splitter (PBS) enables non-mechanical, high-speed beam switching between two orthogonal line-of-sight (LOS) directions for horizontal wind measurement. Systematic tests are performed in controlled wind fields within Mie-dominated and Rayleigh-dominated regimes. The lidar effectively captures the sharp radial velocity profiles at wind speeds up to 7.6 m/s. Comparative experiments with a reference anemometer show that the system delivers reliable performance at 0.48 m range resolution, with measurement uncertainty below 0.34 m/s. With its compact, lightweight, and high-precision design, the developed lidar demonstrates reliable wind measurement capability under laboratory conditions, indicating its potential for future deployment on stratospheric airships.