## 1 Introduction  
Climate change has become one of the most significant global challenges of our time. The application of machine learning (ML) methods provides the ability to not only process and analyze huge amounts of information but also to reveal patterns that are inaccessible using other tools. This enables scientists to better understand the impact of a variety of factors on climate processes and make more accurate forecasts. Modern hardware and software tools allow us to deepen the understanding of atmospheric processes [1, 2] and improve forecasting in the field of environmental engineering [3, 4, 5], and they are even used to optimize power systems [6]. Cloudiness is the most important regulator of the Earth's radiation budget. However, a wide range of questions related to the evaluation of its influence on the amount of solar energy reaching the Earth's surface remains unresolved. High-level clouds (HLCs) are known for their large horizontal extent, which can be up to thousands of kilometers [7; 8]. For this reason, they significantly affect the regional and global radiation budget and climate due to the effects of attenuation and reflection of radiation [9; 10].  
Neural networks (NNs) can be a powerful tool in solving climate problems, but they are not a universal solution for all tasks and must be used in combination with other research and data analysis techniques to achieve the best results. In order for a NN to be able to solve a problem, it needs to be properly trained on a sufficiently large amount of high-quality data, and it needs to take into account all factors affecting the problem being solved. When using NNs in climate research, it should be recognized that they can be ineffective when important climate factors have not been included in the initial dataset, which can lead to incorrect conclusions.  
Thus, in order to achieve the best results, it is necessary to use multimodal studies that include different methods of data investigation and analysis. The present research describes this approach: we investigated the special features of the NN applicability to the problem of reconstructing the relationship between meteorological parameters and optical characteristics of HLCs. The existing atmospheric models estimate the microstructure of HLCs quite roughly: a real ice particle is replaced by an equivalent sphere with a certain value of the effective radius, which negatively affects the accuracy of weather and climate forecasts [11; 12]. The special feature of our approach is the use of the set of atmosphere lidar sensing data from 2009 to the present, which combines more than 3 thousand series of measurements of atmospheric parameters and is systematically expanded. The lidar was developed and built at the National Research Tomsk State University (NR TSU) [13]. Lidar methods are the most promising in solving the tasks of operative control and monitoring of the atmosphere state since they allow vertical profiles of optical, microphysical, and meteorological parameters to be determined in real time [14; 15; 16; 17].  
The materials and methods are discussed in Section 2. Section 2.1 contains basic information about the high-altitude matrix polarization lidar (HAMPL). Section 2.2 describes sources of information on meteorological conditions at the altitudes of clouds registered by the lidar. Section 2.3 provides basic information about the software product developed for processing the lidar meteorological dataset based on machine learning methods. Section 3 collects a description of the results of the work. Section 3.1 gives a preliminary analysis of the data. Section 3.2 describes the implementation of data dimensionality reduction. Section 3.3 is devoted to the implementation of the estimation of the HLC detection altitude. Section 3.4 presents the results of the HLC upper- and lower-boundary altitudes, and Section 3.5 presents the estimation of elements of the HLC backscattering phase matrix (BSPM). Section 4 discusses the results of the paper. The conclusion is given in Section 5.