Scaling Properties of Meteorological Time Series Using Detrended Fluctuation Analysis

Meteorological parameters depend on a diversity of natural processes and show random fluctuations on different temporal and spatial scales as a result of the relevant complex natural processes. A powerful tool for examining these fluctuations is the Detrended Fluctuation Analysis (DFA), which detects long-term correlations in nonstationary time series. In this study, we apply the DFA method to daily meteorological time series (i.e. temperature, pressure, relative humidity and wind speed) for the Thessaloniki surface weather station from January 1973 to December 2014. By examining long-range correlations, we detect if the time series exhibit long and/or short range "memory". Moreover, we compare the behavior of these time series from the aspect of DFA, focusing on the observed similarities or differences of the relevant findings.