Abstract
We propose a method of the modeling and analysis of ionospheric parameters by combining wavelet transform with autoregression models (integrated moving average). The method makes it possible to reveal regularities in ionospheric parameters and to make forecasts on variations. Also, this method can be used to fill the gaps in ionospheric parameters, with consideration of their diurnal and seasonal variations.
The method was tested on foF2 data and data on the total electron content for the regions of Kamchatka and Magadan. The models constructed for the natural variation in ionospheric parameters allowed us to analyze its dynamical mode and build a forecast with a step of up to five hours. Based on estimates for model errors, we revealed anomalies arising during periods of increased solar activity and strong earthquakes in Kamchatka.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Afraimovich, E.L. and Perevalova, N.P., GPS-monitoring verkhnei atmosfery Zemli (GPS-Monitoring of the Earth’s Upper Atmosphere), Irkutsk: GU NU RVKh VSNTs SO RAMN, 2006.
Box, G. and Jenkins, G., Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day, 1970.
Deminov, M.G., Ionosfera Zemli. Plazmennaya geliogeofizika (The Earth’s Ionosphere. Plasma Heliogeophysics), Moscow: Fizmatlit, 2008, vol. 2.
Donoho, D. and Johnstone, I., Minimax estimation via wavelet shrinkage, Ann. Stat., 1998, vol. 26, no. 3, pp. 879–921.
Geppener, V.V. and Mandrikova, O.V., Combination of parametric and nonparametric approaches to the construction of models of nonstationary times series with a complex structure to improve the quality of their processing, Izv. S.-Peterb. Gos. Elektrotekh. Univ. Inst. im. V.I. Ul’yanova, Ser.: Inf. Upr. Komp. Tekhnol., 2003, no. 2, pp. 14–17.
Liperovskaya, E.V., Liperovskii, V.A., and Pokhotelov, O.A., Disturbances in the F-region of the ionosphere before earthquakes, Geofiz. Issled., 2006, no. 6, pp. 51–58.
Mallat, S., A Wavelet Tour of Signal Processing, San Diego: Academic Press, 1998.
Mandrikova, O.V., Polozov, Yu.A., and Zaliaev, T.L., Methods of analysis and interpretation of ionospheric critical frequency foF2 data based on wavelet transform and neural networks combination, Proc. of the 33rd General Assembly of the European Seismological Commission (GA ESC 2012), August 19–24, Moscow, Russia. http://www.esc2012-moscow.org/files/GA_ESC_2012-Program_13.08.2012.xls.
Mandrikova, O.V. and Polozov, Yu.A., A Method for identifying anomalous features in data of the critical frequency of the ionosphere on the basis of combination of wavelet-transform and neural networks, Tsifrovaya Obrab. Signalov, 2012a, no. 2, pp. 29–35.
Mandrikova, O.V. and Polozov, Yu.A., Criteria for selecting wavevlet-functions in problems of approximation of natural time series with complex structure, Inf. Tekhnol., 2012b, no. 1, pp. 31–36.
Mandrikova, O.V., Bogdanov, V.V., and Solov’ev, I.S., Wavelet analysis of geomagnetic field data, Geomagn. Aeron., 2013, vol. 53, no. 2, pp. 268–273.
Mandrikova, O.V., Glushkova, N.V., and Polozov, Yu.A., Algorithms for identifying and analyzing the critical frequency of the ionosphere foF2 on the basis of combination of wavelet-transform and autoregressive models, Tsifrovaya Obrab. Signalov, 2013, no. 1, pp. 47–53.
Marple Jr., S.L., Digital Spectral Analysis with Applications, Englewood Cliffs, N.J.: Prentice-Hall, 1987.
Nikiforov, I.V., Posledovatel’noe obnaruzhenie izmeneniya svoistv vremennykh ryadov (Sequential Detection of Changes in Time Series Features), Moscow: Nauka, 1983.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © O.V. Mandrikova, N.V. Glushkova, I.V. Zhivet’ev, 2014, published in Geomagnetizm i Aeronomiya, 2014, Vol. 54, No. 5, pp. 638–645.
Rights and permissions
About this article
Cite this article
Mandrikova, O.V., Glushkova, N.V. & Zhivet’ev, I.V. Modeling and analysis of ionospheric parameters by a combination of wavelet transform and autoregression models. Geomagn. Aeron. 54, 593–600 (2014). https://doi.org/10.1134/S0016793214050107
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0016793214050107