  A methodology of adaptive time series analysis based on Empirical Mode
Decomposition (EMD) has been employed to investigate $^{7}$Be activity
concentration variability, along with temperature. Analysed data were sampled
at ground level by 28 different stations of the CTBTO-IMS network. The adaptive
nature of the EMD algorithm allows it to deal with data that are both nonlinear
and non-stationary, making no a priori assumptions on the expansion basis. Main
purpose of the adopted methodology is to characterise the possible presence of
a trend, occurrence of AM-FM modulation of relevant oscillatory modes,
residuals distributions and outlier occurrence. Trend component is first
estimated via simple EMD and removed. The recent time varying filter EMD
(tvf-EMD) technique is then employed to extract local narrow band oscillatory
modes from the data. To establish their relevance, a denoising step is then
carried out, employing both the Hurst exponent as a thresholding parameter and
further testing their statistical significance against white noise. The ones
that pass the denoising step are considered to be meaningful oscillatory modes
of the data, and their AM-FM modulation is investigated. Possible applications
of the adopted methodology regarding site characterisation and suggestions for
further research are given in the conclusions.
