RESULTS OF SNOW COVER FORECAST IN THE CAUCASUS REGION USING THE METHOD OF SINGULAR-SPECTRAL ANALYSIS

Introduction: Changes in the mode of winter precipitation and snow cover can be considered as a complex indicator of the climate of the cold season, reflecting changes in the temperature regime, precipitation mode, the frequency of thaws, etc. Water reserves in the snow play a decisive role during the spring flood, affect soil moisture during the sowing of spring crops and the growth of winter crops. The importance of knowing the patterns of distribution of precipitation during the cold period for assessing the agro-climatic resources of the republic, which includes snow cover, should be emphasized. Materials and methods of research: The forecast of changes in snow cover characteristics is no less important than the forecast of climate changes (temperature and liquid precipitation). In this work, based on the meteorological data provided by the North Caucasian UGMS, we obtained the averaged series of the average decade height of snow cover and the number of days with snow cover for the south of the European territory of Russia (ETR). Using the method of singular-spectral analysis ("Caterpillar" -SSA), the dynamics were analyzed and the prognostic capabilities of the SSA method for the height of snow cover and the number of days with snow cover in the south of ETR were investigated. The SSA method is a tool for analyzing and predicting one-dimensional and multidimensional time series. On the basis of the T-test, the effectiveness of the recurrent R-SSA forecast of the average annual height of snow and the number of days with snow cover is shown. Results of the study and their discussion: For all the meteorological quantities considered, the periodicity of their changes, the standard deviation, the maximum deviation and the relative error were obtained. As a result of the selection of the main components (1, 3, and 13), prognostic trends of changes in the studied variables were obtained, periods of their increase and decrease were revealed, and predicted values of the average decade height of snow cover and the number of days for the period 2018-2022 were obtained. Conclusions: As a result of the use of the method of singular-spectral analysis, the forecast of such snow cover characteristics of the southern ETR, such as the average decade height of the snow cover and the number of days with snow cover of the southern ETR for 2018-2022, was made. The identified general trends in the studied characteristics of snow cover in the south of the ETR for the period up to 2022 allow characterizing regional climate changes in the south of the European part of Russia as an integral part of contemporary global warming.

высота снежного покрова, сингулярно-спектральный анализ, прогноз, главные компоненты, остатки модели, Т-тест, snow depth, singular-spectral analysis, prediction, main components, model residuals, T-test

1. IPCC. Summary for Policy-makers of the Special Report “Managingthe Risks of Extreme Events and Disasters to Advance Climate Change Adaptation,” Ed. by C. B. Field, V. Barros, T. F. Stocker, et al.

2. Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (Cambridge, New York, USA, 2012).

3. Roshydromet's Second Assessment Report on Climate Change and its Consequences on the Territory of the Russian Federation.(2013) Rosgidromet, Moscow. 1009.

4. th Session of the Commission for Climatology (CCl-16), Heidelberg,Germany, 3-8 July 2014. Resource: www.ccl-16.wmo.int

5. Ашилова А.А., Кешева Л.А. Анализ высоты снежного покрова в предгорной части Северного Кавказа // Международная научная конференция по региональным проблемам гидрометеорологии и мониторинга окружающей среды. Казань, 2012. C. 206-207.

6. Ташилова А.А., Кешева Л.А., Пшихачева И.Н., Таубекова З.А. Возможности прогнозирования временного климатического ряда с помощью метода «Гусеница» -SSA // Сборник научных трудов Международной научной конференции с элементами научной школы «Инновационные методы и средства исследований в области физики атмосферы, гидрометеорологии, экологии и изменения климата». Ставрополь, 2013. С. 86-89.

7. Структурный анализ и прогноз зимних осадков методом «Сaterpillar»-SSA / А.А. Ташилова [и др.] // Доклады АМАН. 2013. №1. С. 103-106.

8. Голяндина Н.Э. Метод «Гусеница» -SSA Прогноз временных рядов. СПб: Изд-во СПбГУ, 2004. 52 с.

9. Сaterpillar-SSA Copyright © 1996-2018 GistaT Group. All rights reserved. Access mode: http://www.gistatgroup.com (date of application: 20.02.2018).