ANGEO Annales Geophysicae ANGEO Ann. Geophys. 1432-0576 Copernicus Publications Göttingen, Germany 10.5194/angeo-32-563-2014 A method to identify aperiodic disturbances in the ionosphere Wang J.-S. 1 Chen Z. 1 2 Huang C.-M. 2 National Center for Space Weather, China Meteorological Administration, Beijing 100081, China School of Electronic Information, Wuhan University, Hubei 430072, China 26 05 2014 32 5 563 569 Copyright: © 2014 J.-S. Wang et al. 2014 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://angeo.copernicus.org/articles/32/563/2014/angeo-32-563-2014.html The full text article is available as a PDF file from https://angeo.copernicus.org/articles/32/563/2014/angeo-32-563-2014.pdf

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.

 References Billingsley, P.: Probability and Measure, John Wiley and Sons, New York, Toronto, London 1979. Bruce, J. W. and Giblin, P. J.: Curves and Singularities, Cambridge University Press, 2nd Edn., 99–131, ISBN 0-521-42999-4, 1992. Eldar, Y. C. and Oppenheim, A. V.: MMSE whitening and subspace whitening, IEEE Trans. Inform. Theory, 49, 1846–1851, 2003. Fuller-Rowell, T. J., Codrescu, M. V., Moffett, R. J., and Quegan, S.: Response of the thermosphere and ionosphere to geomagnetic storms, J. Geophys. Res., 99, 3893–3914, <a href="http://dx.doi.org/10.1029/93JA02015">https://doi.org/10.1029/93JA02015</a>, 1994. Gonzalez-Velasco, E. A.: Fourier Analysis and Boundary Value Problems, University of Massachusetts Press, 1995. Kagan, A. and Shepp, L. A.: Why the variance?, Stat. Probabil. Lett., 38, 329–333, <a href="http://dx.doi.org/10.1016/S0167-7152(98)00041-8">https://doi.org/10.1016/S0167-7152(98)00041-8</a>, 1998. Kamide, Y. and Chian, A. C.-L.: Handbook of the Solar-Terrestrial Environment, Springer-Verlag Berlin Heidelberg, 2007. Liu, L., Wan, W., Chen, Y., and Le, H.: Solar activity effects of the ionosphere: A brief review, Chin. Sci. Bull., 56, 1202–1211, <a href="http://dx.doi.org/10.1007/s11434-010-4226-9">https://doi.org/10.1007/s11434-010-4226-9</a>, 2011. Matuura, N.: Theoretical models of ionospheric storms, Space Sci. Rev., 13, 124–189, 1972. Perrone, L. and Di Franceschi, G.: Solar, Ionospheric, and Geomagnetic Indices, Annali di Geofisica, 41, 843–855, 1998. Piggott, W. R. and Rawer, K. (Eds.): URSI Handbook of Ionogram Interpretation and Reduction, 2nd Edn., Report UAG-23,WDC-A for STP, NOAA, Boulder, Colorado, 1972. Prölss, G. W.: On explaining the local time variation of ionospheric storm effects, Ann. Geophys., 11, 1–9, 1993. Prölss, G. W.: Ionospheric F-region storms, in Hand-book of Atmospheric Electrodynamics, CRC Press, Boca Raton, Fla., 2, 195–248, 1995. Rice, J. A.: Mathematical statistics and data analysis, Duxbury Press, 2nd Edn., 163-164,ISBN 0-534-20934-3, 1995. Rishbeth, H. and Mendillo, M.: Patterns of ionospheric variability, J. Atmos. Sol. Terr. Phys., 63, 1661–1680, 2001. Rush, C. M.: An ionospheric observation network for use in short-term propagation predictions, Telecommunication J., 43, 544–549, 1976. Tsagouri, I., Belehaki, A., Moraitis, G., and Mavromichalaki, H.: Positive and negative ionospheric disturbances at middle latitudes during geomagnetic storms, Geophys. Res. Lett., 27, 3579–3582, 2000. Zhao, B., Wan, W., Liu, L., Yue, X., and Venkatraman, S.: Statistical characteristics of the total ion density in the topside ionosphere during the period 1996–2004 using empirical orthogonal function (EOF) analysis, Ann. Geophys., 23, 3615–3631, <a href="http://dx.doi.org/10.5194/angeo-23-3615-2005">https://doi.org/10.5194/angeo-23-3615-2005</a>, 2005. Zhao, B., Wan, W., Liu, L., Igarashi, K., Nakamura, M., Paxton, L. J., Su, S.-Y., Li, G., and Ren, Z.: Anomalous enhancement of ionospheric electron content in the Asian-Australian region during a geomagnetically quiet day, J. Geophys. Res., 113, A11302, <a href="http://dx.doi.org/10.1029/2007JA012987">https://doi.org/10.1029/2007JA012987</a>, 2008.