Atmospheric turbulence limits the angular resolution of ground-based optical telescopes. The daytime turbulence conditions for solar observations are stronger and more complicated than the turbulence observed at night. The Baikal Astrophysical Observatory is the site of the 1-m Large Solar Vacuum Telescope (LSVT) located near Lake Baikal (East Siberia, Russia), which is the largest freshwater lake in the world. The region hosts unique weather regimes and natural phenomena, including local winds and giant ice rings. Because the LSVT has ongoing and planned programmes in adaptive optics (AO), statistical knowledge of atmospheric turbulence and wind speed distributions is essential for designing and optimizing AO systems. We present the first seasonal study of the vertical distribution of wind speed and daytime optical turbulence conditions at the Baikal Astrophysical Observatory. Site measurements of the daytime Fried parameter were collected using the Shack–Hartmann wavefront sensor in the LSVT AO system. Reanalysis data from the National Centers for Environmental Prediction (NCEP) and the National Centers for Atmospheric Research (NCAR) were used to characterize the wind speed distribution. The results demonstrate seasonal variation in both solar seeing and wind speed profile. The strongest wind speed was detected in winter and in November, while the weakest wind speed occurred during summer. The strongest daytime turbulence conditions were observed in the winter. The best solar seeing β₀ ≈ 1 arcsec was detected in the summer.

In this study, we apply machine learning to predict optical turbulence in the surface layer at the Baikal Astrophysical Observatory. Advance knowledge of optical turbulence is important for maximizing the efficiency of adaptive optics systems, telescope operations, and the scheduling of the planned observations. Typically, optical turbulence is characterized by the structure constant of the refractive index of air $C_n^2$. The Monin–Obukhov similarity theory (MOST) provides a scientific basis for estimating the structure constant of the refractive index from meteorological variables in the surface layer. However, the MOST becomes unreliable for stable atmospheric conditions, which occurred for more periods regardless of the time of day at the Baikal Astrophysical Observatory. We propose the application of a neural network based on the group method of data handling (GMDH), one of the earliest deep-learning techniques, to predict the surface-layer refractive-index structure constant. The magnitudes of the predicted values of the structure constant of the refractive index and measurements are in agreement. Correlation coefficients ranging from 0.79–0.91 for a stably stratified atmosphere have been obtained. The explicit analytical expression is an advantage of the proposed approach, in contrast to many other machine-learning techniques that have a black-box model.

In this paper, we introduce the results of the statistical analysis of atmospheric characteristics at the site of the Big Telescope Alt-azimuthal (BTA) of the Special Astrophysical Observatory (SAO) of the Russian Academy of Science (RAS). The BTA is the largest optical telescope in Eurasia and is located near Mt Pastukhova in the northern part of the Caucasus Mountains, at an altitude of 2070 m above sea level. The atmosphere of the Earth is a major challenge for observing and it limits the quality of astronomical images obtained by ground-based telescopes. The study of the atmosphere above astronomical observatories is important for the planning of observing time, for the optimization of instrument performance and for the development of adaptive optics systems. We discuss the results of a study of the meteorological conditions at the BTA site: total cloud cover, wind speed at the pressure level of 200 hPa, vertical motions, vertical profiles of the wind speed employing data from the ERA-Interim and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis data bases.

Atmospheric conditions have a major impact on the quality of ground-based astronomy observations. Among atmospheric molecular gases, water vapour presents a significant challenge in ground-based astronomical observations in spectral bands ranging from radio to infrared. The impact of water vapour on astronomical observations is expressed in terms of precipitable water vapour (PWV). Water vapour is a greenhouse gas on Earth that plays an important role in various atmospheric processes, including global climate change. In this work, we analyse the long-term (1975–2021) statistics of PWV above two mid-latitude sites: the 6-m Big Telescope Alt-azimuthal in the Caucasus Mountains and the new 3-m Large Solar Telescope in the Sayan Mountains. Both astronomical sites were established over 45 years ago and are located at an altitude of 2000 m above sea level. We use ERA5 – a new-generation European Centre for Medium-Range Weather Forecasts (ECMWF) retrieved atmospheric reanalysis product. First, we evaluate the reliability of the PWV variable provided in the ERA5 data using global navigation satellite system (GNSS) measurement data. We present the analysis of PWV statistics, including seasonal behaviour above the sites in the infrared astronomical observations context. Then we provide long-term changes of PWV for the period 1975–2021 and examine the data set for the statistical significance of trends. Finally we discuss PWV correlation with other atmospheric parameters: surface temperature and total cloud cover in the regions of both sites.

A survey of the development of adaptive optics, its methods, and tools for the elimination of irregular distortions appearing in the process of light propagation in an inhomogeneous medium is presented. Many ideas developed in recent years were put forward rather long ago; however, only now, owing to creation of state-of-the-art hardware components of optoelectronic systems and devices, they begin to be included in the widest fields of science and technology. The work also presents the current status of adaptive optics systems in solar astronomy, as well as some aspects of their use in systems correcting distortions of powerful laser systems. A retrospective analysis of investigations in the V.E. Zuev Institute of Atmospheric Optics in relation to adaptive optics is carried out.

Astroclimatic conditions characterize the quality of an astronomical site. The Terskol Observatory was founded over 42 years ago in 1980. The astronomical site (coordinates 43°16′29″ N, 42°30′03″ E) is located about 10 km from Mt. Elbrus in the northern Caucasus Mountains. The paper presents the results of an analysis of long-term variations from 1980 to 2022 of astroclimatic parameters such as total cloud cover, precipitable water vapor, and wind speed at a level of 200 hPa above the Terskol Observatory using atmospheric ERA5 Reanalysis data from the European Center for Medium-Range Weather Forecast. The probability distributions and seasonal variations of the astroclimatic parameters are also presented. Long-term trends of the astroclimatic parameters are studied with statistically significant trend analysis methods using the Mann–Kendall test and the Sen’s slope test to estimate the magnitude of the changes. The results show non-significant decreases of annual average 200-hPa wind speed and increases in precipitable water vapor, but significant (99 per cent confidence level) long-term decreasing of total cloud cover above the Terskol Observatory.