Geodesic
The analysis of ionospheric parameters during periods of solar events and geomagnetic storms
Vestnik KRAUNC. Fiziko-matematičeskie nauki, no. 4 (2016), pp. 49-55 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

The paper shows the new methods of analysis of ionospheric and magnetic data using developed by the authors models of multicomponent constructions (MCM models). During periods of high solar activity ionosphere and geomagnetic data is analyzed according to ground stations.
Keywords: magnetic storm, ionospheric parameters, data analysis, wavelet transform, neural networks. 
@article{VKAM_2016_4_a7,
     author = {O. V. Mandrikova and Yu. A. Polozov and I. S. Solovev and N. V. Fetisova},
     title = {The analysis of ionospheric parameters during periods of solar events and geomagnetic storms},
     journal = {Vestnik KRAUNC. Fiziko-matemati\v{c}eskie nauki},
     pages = {49--55},
     year = {2016},
     number = {4},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VKAM_2016_4_a7/}
}
TY  - JOUR
AU  - O. V. Mandrikova
AU  - Yu. A. Polozov
AU  - I. S. Solovev
AU  - N. V. Fetisova
TI  - The analysis of ionospheric parameters during periods of solar events and geomagnetic storms
JO  - Vestnik KRAUNC. Fiziko-matematičeskie nauki
PY  - 2016
SP  - 49
EP  - 55
IS  - 4
UR  - http://geodesic.mathdoc.fr/item/VKAM_2016_4_a7/
LA  - ru
ID  - VKAM_2016_4_a7
ER  - 
%0 Journal Article
%A O. V. Mandrikova
%A Yu. A. Polozov
%A I. S. Solovev
%A N. V. Fetisova
%T The analysis of ionospheric parameters during periods of solar events and geomagnetic storms
%J Vestnik KRAUNC. Fiziko-matematičeskie nauki
%D 2016
%P 49-55
%N 4
%U http://geodesic.mathdoc.fr/item/VKAM_2016_4_a7/
%G ru
%F VKAM_2016_4_a7
O. V. Mandrikova; Yu. A. Polozov; I. S. Solovev; N. V. Fetisova. The analysis of ionospheric parameters during periods of solar events and geomagnetic storms. Vestnik KRAUNC. Fiziko-matematičeskie nauki, no. 4 (2016), pp. 49-55. http://geodesic.mathdoc.fr/item/VKAM_2016_4_a7/

[1] Afraimovich E. L. et al., “The use of GPS arrays in detecting the ionospheric response during rocket launchings”, Earth, Planets and Space, 52:11 (2000), 1061-1066 | DOI

[2] Nakamura E., Maruyama T., Shidama Y., “Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan”, Journal of the National Institute of Information and Communications Technology, 56 (2000), 391-406

[3] Watthanasangmechai K. et al., “TEC prediction with neural network for equatorial latitude station in Thailand”, Earth, Planets and Space, 64 (2012) | DOI

[4] 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”, Geomagnetism and Aeronomy, 54:5 (2014), 638-645 | DOI

[5] Bilitza D., Reinisc B. W., “International Reference Ioinosphere 2007: Improvements and new parameters”, Advances in space research, 42 (2008), 599-609 | DOI

[6] Klimenko M. V., “Numerical modeling of the global ionospheric effects of storm sequence on September 9–14, 2005–comparison with IRI model”, Earth, Planets and Space, 64:6 (2012), 433-440 | DOI

[7] Oyekola O. S., Fagundes P. R., “Equatorial F2-layer variations: Comparison between F2 peak parameters at Ouagadougou with the IRI-2007 model”, Earth, Planets and Space, 64:6 (2012), 553-566 | DOI

[8] Zhao X. et al., “A prediction model of short-term ionospheric foF2 based on AdaBoost”, Advances in Space Research, 53:3 (2012), 387-394 | DOI | MR

[9] Kakinami Y. et al., “Ionospheric electron content anomalies detected by a FORMOSAT-3/COSMIC empirical model before and after the Wenchuan Earthquake”, International Journal of Remote Sensing, 31:13 (2010), 3571-3578 | DOI

[10] Mandrikova O. V., et al., “Method of detection of abnormal features in ionosphere critical frequency data on the basis of wavelet transformation and neural networks combination”, A Journal of Software Engineering and Applications, 5:12B (2012), 181-187 | DOI

[11] Kato H. et al., “Development of automatic scaling software of ionospheric parameters”, Journal of the National Institute of Information and Communications Technology, 56 (2009), 465-474

[12] Mandrikova O. V., et al., “Method for modeling of the components of ionospheric parameter time variations and detection of anomalies in the ionosphere”, Earth, Planets and Space, 67 (2015) | DOI

[13] Mandrikova O. V., Glushkova N. V., Polozov Yu. A., “Simulation and analysis of time variations in ionospheric parameters on the basis of wavelet transform and multicomponent models”, Pattern Recognition and Image Analysis, 25:3 (2015), 470-480 | DOI

[14] Mandrikova O. V., et al., “Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models”, Annals of geophysics, 58:5 (2015)

[15] Box G. E. P., Jenkins G. M., Time Series Analysis Forecasting and Control, Holden-Day, San Francisco, 1970, 537 pp. | MR | Zbl

[16] Mandrikova O. V., Solovev I. S., Zalyaev T. L., “Methods of analysis of geomagnetic field variations and cosmic ray data”, Earth, Planets and Space, 66:2014

[17] Mandrikova O. V., Polozov Yu. A., “Metod vydeleniya anomal'nykh osobennostey v dannykh kriticheskoy chastoty ionosfery na osnove sovmeshcheniya veyvlet-preobrazovaniya i neyronnykh setey”, Tsifrovaya obrabotka signalov, 2 (2012), 29-35

[18] Mallat S., A wavelet tour of signal processing, Academic Press, London, 1999, 637 pp. | MR | Zbl

[19] Buresova D., Lastovicka J., “Pre-storm enhancements of foF2 above”, Advances in Space Research, 39 (2007), 1298–1303 | DOI

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