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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 32, issue 5
Ann. Geophys., 32, 563–569, 2014
https://doi.org/10.5194/angeo-32-563-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 32, 563–569, 2014
https://doi.org/10.5194/angeo-32-563-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Regular paper 26 May 2014

Regular paper | 26 May 2014

A method to identify aperiodic disturbances in the ionosphere

J.-S. Wang1, Z. Chen1,2, and C.-M. Huang2 J.-S. Wang et al.
  • 1National Center for Space Weather, China Meteorological Administration, Beijing 100081, China
  • 2School of Electronic Information, Wuhan University, Hubei 430072, China

Abstract. In this paper, variations in the ionospheric F2 layer's critical frequency are decomposed into their periodic and aperiodic components. The latter include disturbances caused both by geophysical impacts on the ionosphere and random noise. The spectral whitening method (SWM), a signal-processing technique used in statistical estimation and/or detection, was used to identify aperiodic components in the ionosphere. The whitening algorithm adopted herein is used to divide the Fourier transform of the observed data series by a real envelope function. As a result, periodic components are suppressed and aperiodic components emerge as the dominant contributors. Application to a synthetic data set based on significant simulated periodic features of ionospheric observations containing artificial (and, hence, controllable) disturbances was used to validate the SWM for identification of aperiodic components. Although the random noise was somewhat enhanced by post-processing, the artificial disturbances could still be clearly identified. The SWM was then applied to real ionospheric observations. It was found to be more sensitive than the often-used monthly median method to identify geomagnetic effects. In addition, disturbances detected by the SWM were characterized by a Gaussian-type probability density function over all timescales, which further simplifies statistical analysis and suggests that the disturbances thus identified can be compared regardless of timescale.

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