Table of Contents
Journal of Climatology
Volume 2013, Article ID 313917, 6 pages
Research Article

Time Series Analysis: A New Methodology for Comparing the Temporal Variability of Air Temperature

1Institute of Physics, University of Tartu, 50090 Tartu, Estonia
2Tartu Observatory, 61602 Tõravere, Estonia

Received 15 May 2013; Accepted 25 September 2013

Academic Editors: E. Paoletti, A. Rutgersson, and A. P. Trishchenko

Copyright © 2013 Piia Post and Olavi Kärner. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Temporal variability of three different temperature time series was compared by the use of statistical modeling of time series. The three temperature time series represent the same physical process, but are at different levels of spatial averaging: temperatures from point measurements, from regional Baltan65+, and from global ERA-40 reanalyses. The first order integrated average model IMA(0, 1, 1) is used to compare the temporal variability of the time series. The applied IMA(0, 1, 1) model is divisible into a sum of random walk and white noise component, where the variances for both white noises (one of them serving as a generator of the random walk) are computable from the parameters of the fitted model. This approach enables us to compare the models fitted independently to the original and restored series using two new parameters. This operation adds a certain new method to the analysis of nonstationary series.