Arctic and Antarctica
12+
Journal Menu
> Issues > Rubrics > About journal > Authors > About the Journal > Requirements for publication > Peer-review process > Article retraction > Ethics > Online First Pre-Publication > Copyright & Licensing Policy > Digital archiving policy > Open Access Policy > Article Processing Charge > Article Identification Policy > Plagiarism check policy > Editorial Board > Council of Editors
Journals in science databases
About the Journal
MAIN PAGE > Back to contents
Publications of Gagarin Vladimir Evgen'evich
Arctic and Antarctica, 2024-1
Frolov D.M., Seliverstov Y.G., Koshurnikov A.V., Gagarin V.E., Nikolaeva E.S. - Using Machine Learning to Classify Stratigraphic Layers of Snow According to the Snow Micro Pen Device pp. 1-11

DOI:
10.7256/2453-8922.2024.1.69404

Abstract: The observation of snow cover by the staff of the Geographical Faculty of Moscow State University of the meteorological observatory has long been researched. This article describes the snow accumulation features and the snow cover's stratigraphy. The third cyclone arrived in Moscow on the night of December 14. There had been a large number of snowdrifts since the beginning of the snow accumulation, and the 49 cm mark was recorded at the MSU weather station. The difficulties of classifying layers in the snow column have been investigated by many glaciologists, something that is also considered in this paper. Machine learning methods were used to classify stratigraphic layers in the snow column according to measurements from the snow micro pen device. The ice crystal shapes within the snow column, resulting from metamorphism (rounded, faceted, thawed), exhibit variations in both density and parameters derived from the snow micro pen device data processing. Specifically, MPF(N) represents the average resistance force, SD(N) denotes its standard deviation, and cv signifies its covariance. This diversity allows for the categorization of processed device data and the incorporation of new measurement data without relying on direct manual drilling results. The obtained device data underwent thorough processing. Through comparison with data from direct snow stratigraphy surveys, the stratigraphic layers of the snow column were classified. Subsequently, utilizing the classified data of the device's stratigraphic layers, K-nearest neighbors clustering enabled the classification of new data obtained from the device without the need for additional manual surveys in the future.
Arctic and Antarctica, 2023-1
Frolov D.M., Seliverstov Y.G., Sokratov S.A., Koshurnikov A.V., Gagarin V.E., Nikolaeva E.S. - Investigation of the Spatio-Temporal Heterogeneity of Snow Thickness at the Meteorological Site of the Lomonosov MSU in the Winter of 2022/2023 pp. 1-13

DOI:
10.7256/2453-8922.2023.1.40448

Abstract: This paper presents the results of field studies conducted at the MSU meteorological site for the winter period of 2022/2023. The purpose of the observations was to study the development of the snow column and its spatial variability in one winter season. Field research consisted of analyzing stratigraphic layers of snow and measuring their density. The data obtained made it possible to characterize and evaluate changes in snow layers, structure, and density in spatiotemporal terms. The results of the work are displayed on the graphs of the spatial and temporal variability of the snow cover for 2022/2023. The evolution of the snow column over the winter period is analyzed. The analysis of observations reflects a high spatial and temporal variability of snow cover in winter, which allows not only to evaluate and compare the data obtained with past studies but also to supplement and improve the already available information on the heterogeneity of snow cover.
Arctic and Antarctica, 2022-4
Frolov D.M., Koshurnikov A.V., Gagarin V.E., Nabiev I.A., Dodoboev E.I. - Study of the Cryosphere of the Zeravshan and Hissar Ranges (Tien Shan) pp. 1-10

DOI:
10.7256/2453-8922.2022.4.39279

Abstract: This paper presents brief results of studying the cryosphere of the Zeravshan and Hissar Ranges. At the same time, the rate of change in the area of glaciers over the past almost one hundred years and the presence and degradation of permafrost during this time were considered. The actual description of the numerical method for estimating the depth of soil freezing based on data on the thickness of the snow cover and air temperature was also given. An example of using this numerical method for estimating the depth of soil freezing on the slopes was given to map the cryolithozone of the Zeravshan and Hissar Ranges. According to the calculations, the ground under the snow cover remains frozen on the Anzob Pass from December to April. The power of the accumulated snow cover can reach one and a half meters or more. At the same time, the soil under the snow-covered surface freezes, according to calculations, by an average of 1.5 m. Thus, the proposed method for calculating the dynamics of the depth of soil freezing based on air temperature data and snow cover thickness made it possible to assess soil freezing as a factor of soil stability during the construction of village and avalanche protection structures. Thus, the Anzob Pass belongs to an area of seasonal freezing of rocks. Considering the gradient of the average annual temperature of rocks, we can conclude that permafrost rocks on the Hissar Range can be expected at altitudes of more than 4,000 meters.
Other our sites:
Official Website of NOTA BENE / Aurora Group s.r.o.