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Method for Modeling of Ionospheric Parameters and Detection of Ionospheric Disturbances

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Abstract

The paper proposes an automated method for analyzing ionospheric parameters and detecting ionospheric anomalies. The method is based on a generalized multicomponent model of ionospheric parameters (GMCM) developed by the authors. The model identification is based on an integrated approach combining the wavelet-transform methods with the autoregressive-integrated moving average models (ARIMA models). The paper provides estimates of the method efficiency, describes the operations of detecting ionospheric anomalies and evaluating their parameters. On the example of ionospheric parameter processing (the ionospheric critical frequency (foF2)) for the Kamchatka region, we demonstrate the possibility of applying the method in on-line mode (as data become available to system). On the basis of the method, we detected shot-period anomalous changes proceeding magnetic storms and characterizing the occurrences of oscillatory processes in the ionosphere at the background of increased solar activity. The method has been implemented in the “Aurora” system for complex geophysical data analysis (http://lsaoperanalysis.ikir.ru/lsaoperanalysis.html).

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REFERENCES

  1. M. Nakamura, T. Maruyama, and Y. Shidama, “Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan,” J. Natl. Inst. Inf. Commun. Technol. 56 (3), 391–406 (2009).

    Google Scholar 

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

    Article  Google Scholar 

  3. A. D. Danilov, “F-2 region response to geomagnetic disturbances,” J. Atm. Solar-Terr. Phys. 63 (2), 441–449 (2001).

    Google Scholar 

  4. E. L. Afraimovich and N. P. Perevalova, GPS Monitoring of the Earth’s Upper Atmosphere (Nauchn. Tsentr Rekonstr. Vosstan. Khirurg. Vost.-Sib. Nauchn. Tsentr. Sib. Otd. Ross. Akad. Med. Nauk, Irkutsk, 2006) [in Russian].

    Google Scholar 

  5. L. Liu, W. Wan, M. L. Zhang, and B. Zhao, “Case study on total electron content enhancements at low latitudes during low geomagnetic activities before the storms,” Ann. Geophys. 26 (4), 893–903 (2008). https://doi.org/10.1134/S1990793115050206

    Article  Google Scholar 

  6. L. Liu, W. Wan, M. L. Zhang, B. Zhao, B. Ning, “Prestorm enhancements in NmF2 and total electron content at low latitudes,” J. Geophys. Res. A 113 (02311), 1–12 (2008).

    Google Scholar 

  7. P. L. Saranya, K. Venkatesh, D. S. V. V. D. Prasad, P. V. S. Rama Rao, and K. Niranjan, “Pre-storm behavior of NmF2 and TEC (GPS) over equatorial and low latitude stations in the Indian sector,” Adv. Space Res. 48 (2), 207–217 (2011).

    Article  Google Scholar 

  8. L. F. Chernogor and V. T.Rozumenko, “Earth–Atmosphere–Geospace as an open nonlinear dynamical system,” Radio Phys. Radio Astron. 13 (2), 120–137 (2008).

    Google Scholar 

  9. A. V. Dmitriev, A. V. Suvorova, M. V. Klimenko, V. V. Klimenko, K. G. Ratovsky, R. A. Rakhmatulin, and V. Parkhomov, “Predictable and unpredictable ionospheric disturbances during St. Patrick’s Day magnetic storms of 2013 and 2015 and on 8–9 March 2008: Prediction of ionospheric disturbances,” J. Geophys. Res. Space Phys. 122 (2), 2398–2423 (2017).

    Article  Google Scholar 

  10. D. Bilitza and B. W. Reinisch, “International reference ionosphere 2007: Improvements and new parameters,” Adv. Space Res. 42, 599–609 (2008).

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. X. Zhao, B. Ning, L. Liu, and G. Song, “A Prediction model of short-term ionospheric foF2 based on AdaBoost,” Adv. Space Res. 53 (3), 387–394 (2014). https://doi.org/10.1016/j.asr.2013.12.001.2014

    Article  Google Scholar 

  13. V. Sai Gowtam and S. Ram Tulasi, “An artificial neural network-based ionospheric model to predict NmF2 and hmF2 using long-term data set of FORMOSAT-3/COSMIC radio occultation. Observations: Preliminary results,” J. Geophys. Res.: Space Phys. 122 (2017). https://doi.org/10.1002/2017JA024795

  14. A. Tebabal, S. M. Radicella, M. Nigussie, B. Damtie, B. Nava, and E. Yizengaw, “Local TEC modelling and forecasting using neural networks,” J. Atm. Solar-Terr. Phys. 172, 143–151 (2018). https://doi.org/10.1016/j.jastp.2018.03.004

    Article  Google Scholar 

  15. A. Mikhailov, B. Morena, G. Miro, and D. Marin, “A method for foF2 monitoring over Spain using the El Arenosillo digisonde current observations,” Ann. Geophys. 42 (4) (1999). https://doi.org/10.4401/ag-3748

  16. D. V. Solomentsev, A. A. Titov, and B. V. Khattatov, “Three-dimensional assimilation model of the ionosphere for the European region,” Geomagn. Aeron. 53 (1), 73–84 (2013). https://doi.org/10.1134/S0016793212060114

    Article  Google Scholar 

  17. M. A. Knyazeva, A. A. Namgaladze, and K. E. Beloushko, “Field-aligned currents influence on the ionospheric electric fields: Modification of the upper atmosphere model,” Russ. J. Phys. Chem. 9 (5), 758–763 (2015). https://doi.org/10.1134/S1990793115050206

    Article  Google Scholar 

  18. V. N. Shubin, A. T. Karpachev, V. A. Telegin, and K. G. Tchybulya, “Global model SMF2 of the F2-Layer maximum height,” Geomagn. Aeron. 55 (5), 609–622 (2015). https://doi.org/10.1134/S001679321505014X

    Article  Google Scholar 

  19. R. Song, X. Zhang, Ch. Zhou, J. Liu, and J. He, “Prediction TEC in China based on the neural networks optimized by genetic algorithm,” Adv. Space Res. 62 (4) (2018). https://doi.org/10.1016/j.asr.2018.03.043

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

    Article  Google Scholar 

  21. O. V. Mandrikova, N. V. Fetisova (Glushkova), R. T. Al-Kasasbeh, D. M. Klionskiy, V. V. Geppener, and M. Y. Ilyash, “Ionospheric parameter modeling and anomaly discovery by combining the wavelet transform with autoregressive models,” Ann. Geophys. 58 (5) (2015). https://doi.org/10.4401/ag-6729

  22. S. Mallat, A Wavelet Tour of Signal Processing, 3rd ed. (Academic, London, 2008).

    MATH  Google Scholar 

  23. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control (Holden Day, San Francisco, 1976).

    MATH  Google Scholar 

  24. O. V. Mandrikova, N. V. Glushkova, and I. V. Zhivet’ev, “Modeling and analysis of ionospheric parameters by a combination of wavelet transform and autoregressive models,” Geomagn. Aeron. 54 (5), 593–600 (2014). https://doi.org/10.1134/S0016793214050107

    Article  Google Scholar 

  25. O. Mandrikova, Yu. Polozov, N. Fetisova, and T. Zalyaev, “Analysis of the dynamics of ionospheric parameters during periods of increased solar activity and magnetic storms,” J. Atm. Solar-Terr. Phys. 181, 116–126 (2018). https://doi.org/10.1016/j.jastp.2018.10.019

  26. O. Mandrikova, N. Fetisova, and Yu. Polozov, “Method of ionospheric parameter analysis in the problems of real-time data processing,” J. Phys. Conf. Ser. IOP Publ. 1096 (012091) (2018). https://doi.org/10.1088/1742-6596/1096/1/012091

  27. B. R. Levin, Theoretical Foundations of Statistical Radio Engineering (Sov. Radio, Moscow, 1975) [in Russian].

    Google Scholar 

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Funding

The work was carried out according to the Subject АААА-А17-117080110043-4 “Dynamics of physical processes in the active zones of near space and geospheres”. The authors are grateful to the organizations recording the data which were applied in the paper.

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Authors and Affiliations

  1. Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, 648034, Kamchatka oblast, Russia

    O. V. Mandrikova, N. V. Fetisova & Yu. A. Polozov

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  1. O. V. Mandrikova
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  2. N. V. Fetisova
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  3. Yu. A. Polozov
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Correspondence to O. V. Mandrikova, N. V. Fetisova or Yu. A. Polozov.

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Mandrikova, O.V., Fetisova, N.V. & Polozov, Y.A. Method for Modeling of Ionospheric Parameters and Detection of Ionospheric Disturbances. Comput. Math. and Math. Phys. 61, 1094–1105 (2021). https://doi.org/10.1134/S0965542521070137

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