The relation between the orientation of particles in ice-crystal clouds and backscattering phase matrices (BSPMs) is considered. Parameters characterizing the predominant orientation of particles in the azimuthal direction and in the horizontal position are presented. The parameters are expressed through BSPM elements. A technique for measuring BSPM elements with lidar is described. Examples of some measurements are presented along with a statistical generalization of the results from more than 400 BSPM measurements. It is found that the orientation of coarse particles with large diameters in an azimuthal direction and in a horizontal position is more probable than in a random direction. However, the orientation of large particles is often masked by small particles that are not subject to the effect of orienting factors. Thus the mean parameters characterizing the state of orientation of particles in clouds as a whole correspond to weak orientation. It is supposed that the orientation of particles in the azimuthal direction is caused by wind-velocity pulsations.

Remote lidar sensing in the photon-counting mode is now the commonly accepted method for studying atmospheric processes in the lower and free atmosphere. However, when processing signals obtained from lidar measurements, investigators necessarily face the problem of achieving accuracy in reconstructing the atmospheric parameters despite the presence of inhomogeneous noise in the measured signals. We propose an optimal method of linear regression (OMLR) of signals. The accuracy of the the method for the reconstructed signal is estimated. An example of application of the OMLR to the reconstruction of the temperature profile from the data obtained with a Raman lidar at the Siberian Lidar Station of the Institute of Atmospheric Optics (Tomsk, Russia) is given. The proposed method is distinguished by simplicity of interpretation of the criteria used, based on careful adherence to statistical principles. This method is shown to be an efficient auxiliary tool for the processing of measured data.

The special features of the applicability of artificial neural networks to the task of identifying relationships between meteorological parameters of the atmosphere and optical and geometric characteristics of high-level clouds (HLCs) containing ice crystals are investigated. The existing models describing such relationships do not take into account a number of atmospheric effects, in particular, the orientation of crystalline ice particles due to the simplified physical description of the medium, or within the framework of these models, accounting for such dependencies becomes a highly nontrivial task. Neural networks are able to take into account the complex interaction of meteorological parameters with each other, as well as reconstruct almost any dependence of the HLC characteristics on these parameters. In the process of prototyping the software product, the greatest difficulty was in determining the network architecture, the loss function, and the method of supplying the input parameters (attributes). Each of these problems affected the most important issue of neural networks—the overtraining problem, which occurs when the neural network stops summarizing data and starts to tune to them. Dependence on meteorological parameters was revealed for the following quantities: the altitude of the cloud center; elements m₂₂ and m₄₄ of the backscattering phase matrix (BSPM); and the m₃₃ element of BSPM requires further investigation and expansion of the analyzed dataset. Significantly, the result is not affected by the compression method chosen to reduce the data dimensionality. In almost all cases, the random forest method gave a better result than a simple multilayer perceptron.

Methods and means of remote control of the ecological state of the atmosphere are constantly improving. Lidar sensing allows obtaining up-to-date information about natural and technogenic sources of atmospheric pollution. There is a wide range of problems in ecological control, where the deployment of an inexpensive mobile lidar network is required. For this purpose, it is suggested to use Q-switch and MOPA fiber lasers in lidars. Q-switch fiber lasers have a simpler design and are more practical to use. However, pulses from Q-switch lasers have long full-pulse durations. In the present work, a lidar signal inversion method (LSIM) is proposed for solving this problem. Verification and outdoor experimentation of the LSIM was carried out with the reference signal method (RSM). The advantage of the proposed RSM is the minimum number of controllable parameters necessary for LSIM verification and approbation. As a result, the accuracy of the obtained results increased. Thus, the possibility of application of the Q-switch fiber lasers for lidar sensing is shown both theoretically and experimentally.

Remote control of the state of the atmosphere is an urgent problem nowadays. The problem of remote monitoring of the optical parameters of the atmosphere is solved using a matrix polarization lidar (MPL). The scattering matrix obtained from polarization measurements contains complete information on the scattering parameters in the atmosphere. The purpose of the present research is the derivation of the theory and description of methods for solving problems of practical implementation of the multiwave MPL (MMPL). The problem is considered within the framework of the concept of the unified methodological approach to polarization studies. The MMPL operation principle is based on simultaneous use for sensing of the first, second, and third harmonics of radiation of a widespread Nd:YAG laser. The basis for achieving this purpose is provided by new methods of optical selection of the polarization components of radiation received in the experiment, methods of conducting polarization studies, and new solutions in experimental data processing methods. It has been shown that this challenge can be solved within the framework of simple solutions. Thus, the proposed MMPL is structurally simple and compact and can be implemented in mobile polarization lidar systems.

Results of a methodological case study of low-level clouds in the atmosphere using a 1064 nm fiber lidar are presented. The lidar experiment was carried out in Daejeon, Republic of Korea, in January–March 2025. The study’s primary objective was to ascertain the vertical extinction coefficient profiles pertaining to tenuous, low-altitude cloud formations via implementation of a refined Sequential Lidar Signal Processing Algorithm (SLSPA). The SLSPA incorporates statistical estimation theory to assess signal and measurement error. Cloud extinction coefficient profiles are estimated within the SLSPA utilizing the modified Klett–Fernald inversion algorithm. The SLSPA adaptation is required (a) to evaluate the accuracy of Q-switch laser-based lidar sounding signal deconvolution, (b) to mitigate the impact of the lidar form factor on measurement results, (c) to account for aerosol extinction coefficient variability within the cloud in the modified inversion algorithm (MIA), and (d) to evaluate multiple scattering effect correction in the MIA. Theoretical and experimental aspects of the modified SLSPA are considered sequentially in the present work. The experimental results presented here are based on datasets sampled from the entire array of experimental data obtained during the measurement period.