A Robust Seasonality Detector for Geophysical Time Series: Application to Satellite SO2 Observations Over China
We have developed a robust seasonality detector that uses singular spectrum analysis (SSA) and a chi-squared red noise test to extract statistically-significant frequencies from smoothed spectra computed with the discrete Fourier transform (DFT). SSA is found to provide a useful time-series decomposition into a low frequency trend, the total noise and periodicity, but is unable to extract individual cyclical components. We show that it is possible to identify these cycles in the frequency domain by applying a statistical-significance test to the smoothed spectrum such that: (i) spectral estimates at peak frequencies account for the largest proportion of the total variance and (ii) that the peaks are distinct from an equivalent auto-regression AR(1) red noise continuum. We apply this seasonality detector to 141 noisy and often fairly discontinuous time series of monthly mean anthropogenic SO2 loads over major cities and power plants in China extracted from ten years of OMI/Aura satellite observations between 2005 and 2015. We routinely observed the presence of an annual cycle (99 cases) but also a bi-annual cycle (60 cases) in the satellite data. This strong annual and inter-annual variability observed from space is also detected in co-located ground-based SO2 concentrations at the Xinglong observational station in Hebei Province, China.
KeywordsTime Series Discrete Fourier Transform Singular Spectrum Analysis Geophysical Time Series Linear Regression Trend
MEK was supported by funding from the EU FP7 MarcoPolo/Panda project, http://www.marcopolo-panda.eu/.
- Bloomfield P (2000) Fourier analysis of time series: an introduction, second edition: New York, John Wiley & SonsGoogle Scholar
- Ghil M, Allen MR, Dettinger MD et al (2002) Advanced spectral methods for climatic time series, Rev Geophys 40(1:3):1–41Google Scholar
- Koukouli ME, Balis DS, van der A R et al (2016) Characteristics of the anthropogenic SO2 load over China revealed by SCIAMACHY/Envisat, OMI/Aura and GOME2/MetopA (paper in preparation)Google Scholar