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The development of a network of ground-based telescopes requires detailed astroclimatic studies. This paper presents the spatial distributions of precipitable water vapor (PWV), total cloud cover (TCC) and cloud base height (CBH). With the aim of a representative description of the precipitable water vapor, a method for correcting this characteristic which takes into account the underlying surface is proposed. The method uses the exponential decrease in the water vapor content with the altitude and is based on the calculation of the averaged elevation of the grid nodes around the site. By applying this correction method, the seasonal changes in the median PWV values at the sites of Ali, Muztag-Ata and Suffa, as well as within the Chajnantor area are estimated. We show that the decrease of PWV with the altitude is exponential with a height scale of 1000 m for the sites in South America and Eurasia. The astroclimatic characteristics within the Big Telescope Alt-azimuthal (BTA) region (40∘N–50∘N; 35∘E–55∘E) are estimated. In this region, the sites suitable for the millimeter and submillimeter (mm/submm) observations are revealed. New sites are Mt. Horai and Mt. Kurapdag. In addition, we show that the Era-5 reanalysis data overestimate the PWV values by 1–2 mm and describe changes in the monthly medians of PWV. Comparison of the calculated medians with the measured PWV show that the correlation coefficient between these characteristics is 0.97.

The paper presents the results of analysis of astroclimatic conditions in the Big Telescope Alt-azimuthal (BTA) region (40°N–50°N; 35°E–55°E). Using data from the European center for medium-range weather forecast ReAnalysis (ERA-5), we estimated the averaged spatial distributions in total cloud cover, vertical integral of mean kinetic energy, vertical component of wind speed, and wind speed shears, as well as inverse values of Richardson number 1/Ri. An extensive region with the development of atmospheric flows is formed south and southeast of BTA in winter. High inverse values of the Richardson number, spatial heterogeneities in vertical wind speed, and significant wind speed shears in the lower atmosphere are observed in this region. In terms of turbulence development over BTA, the best time for astronomical observations falls in summer, when vertical shears of wind speed are weakened in the lower atmospheric layers. The situation is opposite in the upper troposphere. In winter, BTA is in the region of moderate vertical wind shears. In summer, a region with increased vertical wind speed shears is formed. Taking into account that the intensity of optical turbulence decreases rapidly with height, better image quality can be expected in summer. Such structure of the atmosphere does not allow one to directly apply atmospheric models in order to describe turbulence based on the turbulence strength as function of its ground values, or to use the classical model describing the turbulence velocity as function of air flow velocity at the height corresponding to the 200 hPa level.

This article discusses the quality of astronomical images under conditions of moderate small-scale turbulence and varying meso-scale airflows above the Baikal Astrophysical Observatory (BAO). We applied a Weather Research and Forecasting (WRF) Model, as well as statistical estimations of the Fried parameter from the differential motion of the solar images. The simulations were performed with a fairly high horizontal resolution within a large area of 1600 × 1600 km. A high horizontal resolution provides representative estimations of atmospheric characteristics and correct accounting of large-scale air advection. We considered the influence of atmospheric motions over the cold water area of Lake Baikal, as well as meso-scale vortex structures over rough terrain on solar image quality. A better understanding of structured turbulent small-scale motions and optical turbulence over rough terrain may help to develop advanced methods for diagnostics and prediction of image quality. For the first time, we have shown that the BAO is located at the periphery of a meso-scale atmospheric vortex structure with an anticyclonic direction of airflows in the daytime. An increase in image quality was associated with weakening airflows over Lake Baikal and a decrease in the intensity of wind speed fluctuations. Calculated spectra of atmospheric turbulence in the daytime were close to the classical form. At night and in the morning, the spectra had a steeper slope on small scales. Deformations of the spectra were due to the suppression of turbulence under stable stratification of the atmosphere. The characteristic horizontal scales of the transition from “−5/3” to ∼“−3” spectral slope were 2–2.5 km. The results obtained using the WRF model and analysis of optical turbulence strength (namely, the Fried parameter) indicated that the parameterization schemes used in the WRF model were accurate.

Norm-resolvent convergence with an order-sharp error estimate is established for Neumann Laplacians on thin domains in Rd, d≥2, converging to metric graphs in the limit of vanishing thickness parameter in the “resonant” case. The vertex matching conditions of the limiting quantum graph are revealed as being closely related to those of the δ′ type.

In the present article, we study the possibilities of machine learning for the estimation of seeing at the Maidanak Astronomical Observatory (38∘40′24″ N, 66∘53′47″ E) using only Era-5 reanalysis data. Seeing is usually associated with the integral of the turbulence strength Cn2(z) over the height z. Based on the seeing measurements accumulated over 13 years, we created ensemble models of multi-layer neural networks under the machine learning framework, including training and validation. For the first time in the world, we have simulated optical turbulence (seeing variations) during night-time with deep neural networks trained on a 13-year database of astronomical seeing. A set of neural networks for simulations of night-time seeing variations was obtained. For these neural networks, the linear correlation coefficient ranges from 0.48 to 0.68. We show that modeled seeing with neural networks is well-described through meteorological parameters, which include wind-speed components, air temperature, humidity, and turbulent surface stresses. One of the fundamental new results is that the structure of small-scale (optical) turbulence over the Maidanak Astronomical Observatory does not depend or depends negligibly on the large-scale vortex component of atmospheric flows.

We construct an order-sharp theory for a double-porosity model in the full linear elasticity setup. Crucially, we uncover time and frequency dispersive properties of highly oscillatory elastic composites.

Monitoring the Earth’s ionosphere is an important, fundamental and applied problem. Global Navigation Satellite Systems (GNSS) provide a way of measuring the ionospheric total electron content (TEC), but real-time single-station absolute TEC measurements are still a problem. This study describes a single-station system to measure the absolute TEC, based on the GNSS–MITIGATOR (MonITorInG the Absolute TOtal electRon content) system. The latter enables real-time measurements for the absolute TEC and its derivatives in time and in space to be obtained. The system is implemented by using JAVAD receivers. The convergence time and the run-mode retention time is ~8 h. We provide potential methods for using the system to estimate the critical frequency of the ionosphere, foF2, at oblique paths in the Siberian region. The developed tool could be useful for supporting real-time multi-instrumental ionosphere monitoring or for compensating for the ionospheric errors of radio equipment.

Optical turbulence limits the angular resolution of ground-based astronomical telescopes. The key parameter of optical turbulence is seeing. In this study, seasonal variations of seeing estimated from differential image motion monitor measurements at the Large Solar Telescope site are discussed. The Large Solar Telescope will be located at an elevation of 2000 m above sea level ( 51 ° 37 ′ 18 ″ N, 100 ° 55 ′ 07 ″ E ). The highest seeing values are observed in winter. The median of seeing is 2.″1. In summer, the median decreases to 1.″1. The best atmospheric conditions are observed in April–May, when the medians of seeing are low and the standard deviations are high. During this period, atmospheric situations with low values of seeing (∼0.″5–0.″6) are often observed. We simulated multilayer neural networks for the measured seeing by applying a group method of data handling. Modeled seeing is well described in terms of mean meteorological parameters, which include wind speed components and large-scale vorticity of air flows at different altitudes in the atmosphere. The 12-layer optimal neural network obtained has a high correlation coefficient between modeled and measured seeing values. The linear correlation coefficient is 0.77.

Currently, more than 6000 operating GNSS receivers deliver observations to multiple servers. Ionospheric data are derived from these measurements providing outstanding space coverage and time resolution. There are about 200 million independent measurements daily. Researchers need sophisticated software tools to deal with such a large amount of data. We present recent advances and products from the System for Ionosphere Monitoring and Research from GNSS (SIMuRG). Currently, SIMuRG provides the total electron content (TEC) variations filtered within 2–10 min, 10–20 min, and 20–60 min, the Rate of the TEC Index, the Along Arc TEC Rate index, and the vertical TEC. SIMuRG is an online service at http://simurg.iszf.irk.ru. The system can be used free of charge and allows calculating both maps and series for arbitrary time intervals and geographic regions. All the data products are available in the form of data or figures. We discuss the system and its geophysics applications.

We construct an order-sharp theory for a double-porosity model in the full linear elasticity setup. Crucially, we uncover time and frequency dispersive properties of highly oscillatory elastic composites.