Modeling and analysis of ionospheric parameters based on generalized multicomponent model

N. V. Fetisova   ; O. V. Mandrikova

Vestnik KRAUNC. Fiziko-matematičeskie nauki, Tome 41 (2022) no. 4, pp. 89-106

Résumé

The results of modeling and analysis of ionospheric parameters during magnetic storms in 2017-2021 are presented. We used the critical frequency variations of the ionospheric F2 layer (foF2 ) (according to the ionosonde data from Paratunka site, Kamchatka peninsula, IKIR FEB RAS). The modeling was based on a generalized multicomponent model of ionospheric parameters (GMCM) developed by the authors. GMCM allows us to study in detail the dynamics of ionospheric parameters during disturbed periods. The GMCM identification is based on the combination of wavelet transform and autoregressive models (ARIMA models). The model describes three classes of anomalies characterizing strong (class 3), moderate (class 2) and weak (class 1) ionospheric disturbances. The ionospheric parameter dynamics was studied with respect to the strength of a geomagnetic disturbance (weak, moderate and strong intensity events were considered). On the basis of the modeling, we detected ionospheric anomalies of various intensity and duration. On the eve of moderate and strong magnetic storms, the fact of a high frequency of the pre-increase effect in the ionosphere was noted. It has an important applied significance.

Détail

Keywords: ionospheric disturbances, wavelet-transform, autoregressive model.

Bibliographie

[1] Afraimovich E. L., Perevalova N. P., GPS-monitoring verkhnei atmosfery Zemli., GU NU RVKh VSNTs SO RAMN, Irkutsk, 2006, 480 pp.

[2] Danilov A. D., “F-2 region response to geomagnetic disturbances”, Journal of Atmospheric and Solar-Terrestrial Physics, 63 (2001), 441–449 | DOI

[3] Danilov A. D., “Ionospheric F-region response to geomagnetic disturbances”, Adv. Space Res., 52:3 (2013), 343–366 | DOI

[4] Nakamura M., Maruyama T., Shidama Y., “Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan”, J. Natl. Inst. Inf. Commun. Technol., 56 (2009), 391–406

[5] Chernogor L. F., Rozumenko V. T., “Earth – atmosphere – geospace as an open nonlinear dynamical system”, Radio Phys. Radio Astron., 13:2 (2008), 120–137

[6] Bilitza D., Reinisch B., “International reference ionosphere 2007: improvements and new parameters”, Adv. Space Res., 42:4 (2008), 599–609 DOI: 10.1016/j.asr.2007.07.048 | DOI

[7] Solomentsev D. V., Titov A. A., Khattatov B. V., “Three-dimensional assimilation model of the ionosphere for the European region”, Geomagn. Aeron., 53:1 (2013), 73–84 DOI: 10.1134/S0016793212060114 | DOI

[8] Tebabal A., Radicella S. M., Nigussie M., Damtie B., Nava B., Yizengaw E., “Local TEC modelling and forecasting using neural networks”, J. Atmos. Sol. Terr. Phys., 172 (2018), 143–151 DOI: 10.1016/j.jastp.2018.03.004 | DOI

[9] Watthanasangmechai K., Supnithi P., Lerkvaranyu S., Tsugawa T., Nagatsuma T., Maruyama T., “TEC prediction with neural network for equatorial latitude station in Thailand”, Earth Planets Space, 64:6 (2012), 473–483 | DOI

[10] Mikhailov A., Morena B., Miro G., Marin D., “A method for foF2 monitoring over Spain using the El Arenosillo digisonde current observations”, Ann. Geophys., 42:4 (1999), 683–689 DOI: 10.4401/ag-3748

[11] Knyazeva M. A., Namgaladze A. A., Beloushko K. E., “Field-aligned currents influence on the ionospheric electric fields: modification of the upper atmosphere model”, Russ. J. Phys. Chem., 9:5 (2015), 758–763 DOI: 10.1134/ S1990793115050206 | DOI

[12] Shubin V. N., Karpachev A. T., Telegin V. A., Tchybulya K. G., “Global model SMF2 of the F2-layer maximum height”, Geomagn. Aeron., 55:5 (2015), 609–622 DOI: 10.1134/S001679321505014X | DOI

[13] Mandrikova O. V., Fetisova N. V., Polozov Y. A., Solovev I. S., Kupriyanov M. S., “Method for modeling of the components of ionospheric parameter time variations and detection of anomalies in the ionosphere”, Earth Planets Space, 67:1 (2015), 131–146 DOI: 10.1186/s40623-015-0301-4 | DOI

[14] Mandrikova O., Polozov Yu., Fetisova N., Zalyaev T., “Analysis of the dynamics of ionospheric parameters during periods of increased solar activity and magnetic storms”, J. Atmos. Solar-Terr. Phys., 181 (2018), 116-126 DOI: 10.1016/j.jastp.2018.10.019 | DOI

[15] Mandrikova O., Fetisova N., “Modeling and analysis of ionospheric parameters based on multicomponent model”, J. Atmos. Solar-Terr. Phys., 208 (2020), 105399 DOI: 10.1016/j.jastp.2020.105399 | DOI

[16] Mandrikova O, Fetisova N, Polozov Y., “Hybrid Model for Time Series of Complex Structure with ARIMA Components”, Mathematics, 9:10 (2021), 1122 DOI: 10.3390/math9101122 | DOI

[17] Mallat S., A Wavelet Tour of Signal Processing: 3rd ed., Academic Press, London, 1999, 637 pp.

[18] Box G., Jenkins G., Time Series Analysis: Forecasting and Control, Holden Day, San Francisco, 1970, 537 pp.

[19] Buresova D., Lastovicka J., “Pre-storm electron density enhancements at middle latitudes”, J. Atmos. Sol. Terr. Phys., 70 (2008), 1848–1855 DOI: 10.1016/j.jastp.2008.01.014 | DOI

[20] Loewe C., Prolss G., “Classification and mean behavior of magnetic storms”, J. Geophys. Res., A102 (1997), 14209–14213 DOI: 10.1029/96JA04020 | DOI

[21] Abdu M. A., “Major phenomena of the equatorial ionosphere-thermosphere system under disturbed conditions”, J. Atmos. Sol. Terr. Phys., 59 (1997), 1505–1519 DOI: 10.1016/S1364-6826(96)00152-6 | DOI

[22] Blagoveshchensky D. V., Kalishin A. S., “Increase in the critical frequency of the ionospheric F region prior to the substorm expansion phase”, Geomagn. Aeron., 49 (2009), 200–209 DOI: 10.1134/S0016793209020091 | DOI

[23] Danilov A. D., Konstantinova A. V., “Ionospheric precursors of geomagnetic storms. 1. A review of the problem”, Geomagn. Aeron., 59:5 (2019), 554–566 DOI: 10.1134/S0016793219050025 | DOI

[24] Konstantinova A. V., Danilov A. D., “Ionospheric precursors of magnetic storms. 2. Analysis of Slough station data”, Geomagn. Aeron., 60 (2020), 311–317 DOI: 10.1134/S001679322003010X | DOI