Anton A. Doroshkevich

Subject of study. The polarization characteristics of scattered solar radiation in the presence and absence of high-level clouds (HLCs) are investigated. Aim of study. The aim is to develop a technique for detecting and identifying HLCs containing horizontally oriented ice particles based on passive measurements of the polarization characteristics of scattered solar radiation at the zenith. Method. The polarization characteristics of scattered solar radiation obtained from passive (polarimetric) measurements are analyzed in conjunction with the optical and geometric characteristics of HLCs determined simultaneously from active polarization (lidar) observations. Main results. Polarimetric measurements of scattered solar radiation at the zenith, performed with simultaneous monitoring of the atmospheric aerosol state, including HLCs, using a matrix polarization lidar, are presented. Under clear-sky conditions, the expected consistency between the polarization degree of scattered solar radiation and the solar zenith angle is observed. In the presence of HLCs, deviations from a smooth diurnal variation caused by the multilayer cloud structure are revealed. Practical significance. The results confirm the feasibility of using passive polarimetry for diagnosing the state of cloud cover and atmospheric aerosol layers. They also demonstrate the need for the further development of polarimetric methods and their integration with active atmospheric sensing techniques. Improvement of these methods will contribute to increased accuracy in climate research.

Subject of study. The optical characteristics of high-level clouds (HLCs), which significantly affect the Earth’s radiation budget and climate due to their large spatial extent, are investigated. Aim of study. This study aims to develop an approach for the comprehensive study and assessment of HLC characteristics. In addition, the study seeks to determine the conditions and frequency of HLC occurrence by comparing satellite, lidar, and meteorological data, thereby broadening the geophysical applicability of the results. Method. Data from polarization lidar sensing, the MODIS satellite spectroradiometer, radiosonde observations, weather stations, and atmospheric reanalysis datasets ERA5 and MERRA-2 are used. These reanalysis datasets combine numerical atmospheric modeling with heterogeneous observations, providing temporally and spatially consistent datasets. Main results. A lidar dataset for 2009–2024 is described, together with the distributions of HLC characteristics derived from it. Atmospheric data sources suitable for predicting the formation of HLCs and their characteristics, including cirrus with a preferred horizontal orientation of ice crystals, are identified. Practical significance. The results can be used to refine parameters in climate models and short-term cloud forecasts, as well as for analyzing the distribution and dynamics of clouds in various regions. Integration of local lidar observations with large-scale meteorological information based on the analysis of optical characteristics of clouds significantly expands the geophysical applicability of the results.

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.

Interpreting the results of a high-level clouds (HLCs) lidar study requires a comparison with the vertical profiles of meteorological quantities. There are no regular radiosonde measurements of vertical profiles of meteorological quantities in Tomsk. The nearest aerological stations are several hundred kilometers away from the lidar and perform radiosonde measurements only a few times a day, whereas lidar experiments are performed continuously throughout the day. To estimate meteorological conditions at the HLC altitudes, we propose to use the ERA5 reanalysis. Its reliability was tested by comparing with the data from five aerological stations within a radius of 500 km around Tomsk. A labeled database of the lidar, radiosonde, and ERA5 data (2016–2020) for isobaric levels 1000–50 hPa was created. The temperature reconstruction error over the entire altitude range was characterized by an RMSE of 0.8–2.8 °C, bias of 0–0.9, and Corr ~1. The accuracy of the relative vertical profiles (RMSE 25–40%, Bias 10–22%, and Corr <0.7) and specific humidity (RMSE 0.2–1.2 g/kg, Bias ~0 g/kg, and Corr ~0) at the HLC altitudes were unsatisfying. The ERA5 data on wind direction and speed for the HLC altitudes were promising.

This article presents results of the polarization laser studies of the optical and microphysical characteristics of the high-level clouds (HLC). The high-altitude matrix polarization lidar (HAMPL; Tomsk, Russia) is described. HAMPL measures vertical profiles of all elements of the backscattering phase matrix (BSPM) of the HLC. Based on the joint analysis of lidar and radiosonde observations it is shown that the spatial structure of the HLC containing oriented ice crystals is inhomogeneous in the horizontal wind direction. It includes local areas with oriented particles; the sizes of such areas are estimated together with the most probable meteorological conditions of their formation. The shortcomings of the radiosonde observations performed closest to the location of the HAMPL are described. The applicability of the ERA5 reanalysis data of the European Centre for Medium-Range Weather Forecasts for use as an alternative source of information on the vertical profiles of meteorological quantities for the interpretation of HLC lidar sensing data in Western Siberia was checked.

The report deals with laser sensing of crystal clouds in the double scattering approximation. The focus is on analyzing the influence of the size of crystalline particles on the polarization characteristics of double scattering lidar return.

The propagation of optical radiation in optically dense media is accompanied by multiple light scattering, which leads not only to the extinction, but also to its depolarization. The method of polarization laser sensing allows to increase the information content of lidar data. The results of numerical simulation of the distribution in the registration plane of the polarization characteristics of a lidar return due to double scattering from droplet clouds are presented in this paper. The influence of the parameters of the particle size distribution of the droplet cloud on the azimuthal intensity distribution of the lidar return with a change in the polarization state of the probe radiation is discussed.

The work is devoted to polarization remote sensing of droplet clouds in the double-scattering approximation. A numerical simulation of the individual radiation fluxes forming the lidar return is carried out. The main attention is paid to the analysis of the influence of the aerosol formation microstructure on the polarization characteristics of a double scattered radiation.