Annotation
As the scientific community accepts the modern global climate changes, statistical analysis of a time series of hydrometeorological parameters becomes topical. A time series of air temperature was decomposed in this work; the decomposition allows one to distinguish regular, seasonal, and random components and to assess their statistical significance and adequacy to observation results. On the basis of a linear-regression model, a statistically significant increase in the annual average air temperature in the region under study was determined, both for the entire observation period and for separate months of the year.
Received: 2015 April 13
Approved: 2015 November 13
PACS:
02.50.-r Probability theory, stochastic processes, and statistics
© 2016 Publisher M.V.Lomonosov Moscow State University
Authors
V.A. Gazaryan$^{1,2}$, J.A. Kurbatova$^3$, T.A. Ovsyannikov$^1$, N.E. Shapkina$^1$
$^1$Department of Physics, Moscow State University, Moscow, 119991 Russia
$^2$Department of Applied Mathematics and Information Technologies, Financial University under the Government of the Russian Federation, Leningradskii pr. 46, Moscow, 125993 Russia
$^3$Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Leninskii pr. 33, Moscow, 119071 Russia.
$^1$Department of Physics, Moscow State University, Moscow, 119991 Russia
$^2$Department of Applied Mathematics and Information Technologies, Financial University under the Government of the Russian Federation, Leningradskii pr. 46, Moscow, 125993 Russia
$^3$Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Leninskii pr. 33, Moscow, 119071 Russia.