Astroclimatic Statistics at the Sayan Solar Observatory

Shikhovtsev, A. Yu.; Kovadlo, P. G.; Kiselev, Aleksandr

doi:10.12737/stp-61202012

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…where i, j-indices of the subapertures, t-time, s [1] i,j -local slopes of the wavefront on the subaperture i, j of the first Shack-Hartmann wavefront sensor (1), s [2] i+δi,j+δjlocal slopes of the wavefront on the subaperture i + δi, j + δj of the second Shack-Hartmann wavefront sensor (2), O(δi, δj)-the number of crossed subapertures; (v) The turbulence characteristics are estimated using cross-correlation functions and autocovariance function; (vi) Recovering the profiles of turbulent characteristics up to the maximum height is determined by the geometry of light beams from spaced light sources. Heights of crossing beams is estimated using relation (3) [15]:…”

Section: Slodar Techniquementioning

confidence: 99%

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Modified Method to Detect the Turbulent Layers in the Atmospheric Boundary Layer for the Large Solar Vacuum Telescope

et al. 2021

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A method to detect the atmospheric turbulent layers using a single Shack–Hartmann wavefront sensor is discussed. In order to determine the height distribution of the atmospheric turbulence above a telescope, we register the wavefront distortions at different regions of the aperture from a single light solar object moving in time. Changes of the spatial position of the solar object on the sky give us the possibility to estimate the angular shift of an object. Cross-correlation analysis of the low-frequency component of wavefront slopes spaced on the telescope aperture at different times allows us to estimate characteristics for different atmospheric layers. Knowledge of the height profiles of atmospheric turbulence as well as the Fried parameter is critical for wide-field adaptive optics (AO).

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Section: Slodar Techniquementioning

confidence: 99%

“…The paper is aimed at the development of the method to detect the turbulence layers. This is important for the study of the structure of optical turbulence at the sites of the Large Solar Vacuum Telescope of the Baikal Astrophysical Observatory as well as Sayan Solar Observatory [1,2].…”

Section: Introductionmentioning

confidence: 99%

Modified Method to Detect the Turbulent Layers in the Atmospheric Boundary Layer for the Large Solar Vacuum Telescope

et al. 2021

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“…A possible cause of this underestimation may be the overestimated downward short-wave radiation that is taken into account within the computations for cloud coverage [20]. It is worth mentioning, however, that in some particular cases reanalyses may be consistent with meteorological stations data in terms of the low-frequency temporal variability of cloud characteristics [21].…”

Section: Introductionmentioning

confidence: 99%

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

et al. 2021

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Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has been tackled by several generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there has been no considerable progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose a framework for the assessment of the algorithms’ characteristics. We present several new algorithms that are based on deep learning techniques: A model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million all-sky optical images of the visible sky dome taken in various regions of the world ocean. The research campaigns that contributed to the DASIO collection took place in the Atlantic ocean, the Indian ocean, the Red and Mediterranean seas, and the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and test subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver a superior quality compared to all previously published approaches. As a key result, we demonstrate a considerable drop in the ability to generalize the training data in the case of a strong covariate shift between the training and test subsets of imagery which may occur in the case of region-aware subsampling.

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“…After the first artificial Earth satellite was launched in 1957, new opportunities for studying our planet from near-Earth orbits appeared. Satellite monitoring allowed obtaining data on the distribution of air pollutants over large areas with a sufficient degree of reliability and estimating atmospheric characteristics with acceptable accuracy 3,4 . Over the past decades, NO2, NH3, CO, O3, SO2 and particulate matter (PMx) satellite measurement systems have been actively used to determine the global distribution of air pollutants in the atmosphere [5][6][7][8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of satellite and continuous surface measurements of gas impurities in the air basin at the Listvyanka station, Lake Baikal

Molozhnikova

Maxim

Anastasia

et al. 2022

28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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This study demonstrates a comparative analysis of surface and satellite measurements. The average concentrations of PM2.5 and CO, SO2, measured at the Listvyanka station located on the coast of Lake Baikal, were considered. Satellite measurements data (Copernicus Sentinel-5P) were recomputed based on the SILAM model. A joint analysis of data showed that satellite measurements were suitable for a spatial description of regional air pollution. The computed maxima coincided with the surface measurements in terms of time periods and general monitoring results. However, at extreme increases in concentrations of pollutants, a significant difference in the numerical values was registered. Satellite monitoring data confirmed the relationship between the increase in PM2.5 and CO concentrations in the air basin at the Listvyanka station and the transfer of smoke plumes from intense forest fires located at a distance of 1,500 – 2,000 km.

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Astroclimatic Statistics at the Sayan Solar Observatory

Cited by 11 publications

References 9 publications

Modified Method to Detect the Turbulent Layers in the Atmospheric Boundary Layer for the Large Solar Vacuum Telescope

Modified Method to Detect the Turbulent Layers in the Atmospheric Boundary Layer for the Large Solar Vacuum Telescope

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

Comparative analysis of satellite and continuous surface measurements of gas impurities in the air basin at the Listvyanka station, Lake Baikal

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