Accurate measurements of visibility are of great importance in many fields. This paper reports a multipoint visibility measurement (MVM) method to measure and calculate the atmospheric transmittance, extinction coefficient, and meteorological optical range (MOR). The relative errors of atmospheric transmittance and MOR measured by the MVM method and traditional transmissometer method are analyzed and compared. Experiments were conducted indoors, and the data were simultaneously processed. The results revealed that the MVM can effectively improve the accuracy under different visibility conditions. The greatest improvement of accuracy was 27%. The MVM can be used to calibrate and evaluate visibility meters.

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.

Although polarization lidar technology has been widely used to detect depolarized particles, many difficulties still exist in measuring accurate values for the depolarization ratio. This ratio is basically related to the difference between the optical and the electronic gain of the lidar's channels, the polarization purity of the laser pulse, and the error in the alignment between the laser's polarization plane and the polarizer. We propose here an algorithm for the calibration and calculation of polarization lidars. Numerical simulations showed that the depolarization ratio profile retrieved from the proposed algorithm agrees well with the simulated profile. The proposed algorithm no longer requires the laser source to be linearly polarized. Instead, the light polarization can be elliptical or random. During the installation process, the polarization of the detector is no longer required to be parallel or perpendicular to the polarization direction of the laser source and can be done in any direction.

We report the development of a portable coherent Doppler lidar (CDL) system incorporating onboard field programmable gate array for real-time data processing. An ordinal statistic-based mean Doppler frequency estimation algorithm with high accuracy and low computational cost is proposed. The basic characteristics of the proposed method were specified with the use of mathematical modeling through the algorithm, realized as MATLAB computer code that includes parts simulating lidar signals and data processing. A comparison of the proposed method with established and well-used determination methods of the maximum Doppler frequency is accomplished. The ordinal statistics method is easy to implement and facilitates obtaining computational results swiftly. To demonstrate the performance of this lidar system, an intercomparison field campaign between lidar and ultrasonic anemometer is carried out. The excellent degree of correlation experimentally confirmed the feasibility of developing affordable, low-power consumption, compact, and robust CDL systems for various industrial and scientific applications.