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A Robust Seasonality Detector for Geophysical Time Series: Application to Satellite SO2 Observations Over China

  • M. Taylor
  • M. E. Koukouli
  • N. Theys
  • J. Bai
  • M. M. Zempila
  • D. Balis
  • M. van Roozendael
  • R. van der A
Conference paper
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)

Abstract

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.

Keywords

Time Series Discrete Fourier Transform Singular Spectrum Analysis Geophysical Time Series Linear Regression Trend 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

MEK was supported by funding from the EU FP7 MarcoPolo/Panda project, http://www.marcopolo-panda.eu/.

References

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • M. Taylor
    • 1
    • 2
  • M. E. Koukouli
    • 2
  • N. Theys
    • 3
  • J. Bai
    • 4
  • M. M. Zempila
    • 2
    • 5
  • D. Balis
    • 2
  • M. van Roozendael
    • 3
  • R. van der A
    • 6
  1. 1.Atmospheric Physics and Chemistry GroupNational Observatory of AthensAthensGreece
  2. 2.Laboratory of Atmospheric PhysicsAristotle University of ThessalonikiThessalonikiGreece
  3. 3.Belgian Institute for Space Aeronomy, BIRA-IASBBrusselsBelgium
  4. 4.Institute of Atmospheric Physics (IAP-CAS)BeijingChina
  5. 5.Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsUSA
  6. 6.Koninklijk Nederlands Meteorologisch Instituut (KNMI)De BiltThe Netherlands

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