Traditionally, Klett and Fernald inversion estimates an initial value using the slope method for horizontal visibility, which causes inversion uncertainty. We proposed an algorithm to retrieve the extinction coefficient and visibility distribution information from scanning Lidar to overcome instability due to initial atmospheric extinction coefficient choice and assuming the Lidar ratio. Numerical simulations showed that extinction coefficient maximum relative was much larger for inhomogeneous atmosphere using the Klett method, reaching 0.31. In contrast, it is only 0.049 using the proposed algorithm. Experimental showed that the proposed algorithm and scanning Lidar system provide very high stability and accuracy, can work in different weather conditions and monitor sea fog evolution over real time, and is suitable for various situations with different visibility.

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.

A well-designed filter assembly is incorporated to an earlier mobile Rayleigh Doppler Lidar developed at University of Science and Technology of China (USTC) for wind measurement round the clock. The filter assembly consists of two cascaded Fabry-Perot Etalons (FPEs) and a narrow-band interference filter (IF), which are optimized to filter out strong solar background radiation during daytime. The high resolution FPE is mainly used to compress the whole bandwidth of the filter assembly, whereas the low resolution FPE with relatively large free spectral range (FSR) is primarily used to block the unwanted periodic transmission peaks of high resolution FPE arising within the narrow-band IF passband. Some test experiments are carried out and demonstrate that the filter assembly have an overall peak transmission of 33.32% with a bandwidth of 2.41 pm at 355 nm. When applying it to the USTC mobile Rayleigh Doppler Lidar, the daytime background is only 3% or less than before. Consequently, the detectable altitude during daytime increases to ∼51 km with wind velocity accuracy of ±7.6 m/s.

<!-- *** Custom HTML *** --> Coherent Doppler lidar (CDL) has been considered a powerful and reliable tool for horizontal wind shear monitoring since the 1990s. However, radial observations obtained by Doppler lidar are limited in determining sophisticated variations in horizontal wind field and thus the wind shear. In our work, this problem is solved by applying a statistical interpolation technique to a CDL system, which is implemented at Lanzhou Zhongchuan Airport (103°E, 36°03′N) since August 2019, for routine observation of the horizontal shear along the pathway of the aircraft. By means of that, the horizontal wind vectors are retrieved in real time and the wind shear is also obtained simultaneously. Compared with radial observations, the accuracy of real-time horizontal wind vectors is indicated with the root mean squared error of 0.504 m · s^−  1. During the field campaign, a typical wind shear induced by wind gust is obtained by the CDL system, which moves from south to north with the maximum up to 9.6 m · s^−  1 · km^−  1. Also, five cases resulting in flights going around are described in detail and compared with the reports from the airport terminal control office. The results reveal that the CDL system is quite suitable for wind shear forewarning for demonstrating the time, position, and intensity of wind shear with acceptable accuracy.

Coherent Doppler lidar (CDL) has long been used to automatically identify gust front-induced wind shear signatures from the velocity data, but rare attention has been given to the fine structure of wind gust fronts. In this work, a compact and robust CDL with high efficiency and accuracy is equipped at Lanzhou Airport (103°49 ′ E, 36°03 ′ N) to conduct interpretation of wind gust front structures by using high-resolution CDL data. Outflows of gust fronts could be detected reliably from radial velocities, spectral widths, as well as radial shears. For the case study presented here, photographs of the velocity and spectrum width capacitates gust front characteristics such as height, advance speed, and radial shear, as well as vertical structure to be displayed in minute detail. Besides, the quasi-continuous vertical wind reveals the potential turbulent mixing and vertical transport process during the gust front event, which makes CDL a very attractive and essential technique for future development of gust front automatic detection systems.