A.М. Appalonov1, Yu.S. Maslennikova2

1,2 Kazan Federal University (Kazan, Russia)

1 artem309_97@mail.ru; 2 jsmaslennikova@kpfu.ru

This paper presents the results of the analysis of the spatial distribution of the total electronic content (TEC) performed using the principal component method. For the analysis, global maps of diurnal variations of the TEC for the period from 1999 to 2018, including the 24th and 25th cycles of solar activity, were used. To reduce the influence of daily and seasonal periodicity, preliminary filtering of data was carried out, as well as additional processing of the coordinate grid. Then the processed data was transformed into a correlation matrix. Thus, spatial and temporal components were obtained. The contribution of the main three spatial components to the overall variation of the total electron content of the ionosphere is given. Additionally, it is shown that the first component of the decomposition corresponds to the equatorial anomaly. The paper shows the existence of a correlation between the variations of the first components of the decomposition of TEC (due to the equatorial anomaly of the ionosphere) and the solar activity index F10.7, which confirms the results obtained earlier by other authors on the analysis of the 24 cycle of solar activity.

Appalonov A.М., Maslennikova Yu.S. The analysis of the global dynamics of the total electronic content for 23 and 24 solar activity cycles using the principal component method. Radiotekhnika. 2023. V. 87. № 12. P. 46−55. DOI: https://doi.org/10.18127/j00338486-202312-06 (In Russian)

1. Brjunelli B.E., Namgaladze A.A. Fizika ionosfery. M.: Nauka. 1988. 527 s. (in Russian).
2. Mendillo M. Storms in the ionosphere: Patterns and processes for total electron content. Reviews of Geophysics. 2006. V. 44. № 4. DOI: 10.1029/2005RG000193.
3. Boehm J., et al. The Global Mapping Function (GMF): a new empirical mapping function based on numerical weather model data. GRL. 2006. V. 33. № 7. DOI: 10.1029/2005GL025546.
4. Fitzgerald T.J. Observations of total electron content perturbation of GPS signals caused by a ground level explosion. J. Atoms. S. Terr. Phys. 1997. V. 59. P. 829-834.
5. Nematipour P., Raoofian-Naeeni M., Razin M.R.G. Regional application of C1 finite element interpolation method in modeling of ionosphere total electron content over Europe. Advances in Space Research. 2022. V. 69. № 3. Р. 1351-1365. DOI: 10.1016/j.asr.2021.11.030.
6. Talaat E.R., Zhu X. Spatial and temporal variation of total electron content as revealed by principal component analysis. Ann. Geophys. № 34. Р. 1109–1117. DOI: 10.5194/angeo-34-1109-2016, 2016.
7. Maksimov D.S., Kogogin D.A., Nasyrov I.A., Zagretdinov R.V. Avtomatizirovannaja sistema obrabotki dannyh radiozon-dirovanija signalami navigacionnyh sputnikov, poluchennyh na plotnoj seti GNSS stancij. XVII Vseross. otkrytaja konf. «Sovremennye problemy distancionnogo zondirovanija zemli iz kosmosa». M.: IKI RAN. 2019. S. 487 (in Russian).
8. Bust G.S., Liles W., Mitchell C. Space weather influences on HF, UHF, and VHF radio propagation. Space Weather Effects and Applications. 2021. Р. 153-163. DOI: 10.1002/9781119815570.ch7.
9. Tsagouri I., Koutroumbas K., Elias P. A new short-term forecasting model for the total electron content storm time disturbances. Journal of Space Weather and Space Climate. 2018. № 8. Р. 2–12. DOI: 10.1051/swsc/2018019.
10. Syrovatskij S.V., Jasjukevich Ju.V., Vesnin A.M. i dr. Vlijanie solnechnyh vspyshek na ionosferu Zemli v 24-m cikle solnechnoj aktivnosti. Uchenye zapiski fizicheskogo fakul'teta MGU. 2018. № 4. S. 1840403 (in Russian).
11. Jelektronnyj resurs JPL. URL: https://www.jpl.nasa.gov (data obrashhenija: 15.06.2023) (in Russian).
12. Maslennikova Yu.S., Bochkarev V.V. Principal component analysis of global maps of the total electronic content. Geomagnetism and Aeronomy. 2014. V. 54(2). Р. 216–223. DOI: 10.1134/s0016793214020133.
13. Gorban A.N., Kegl B., Wunsch D., Zinovyev A.Y. (Eds.) Principal manifolds for data visualisation and dimension reduction. Series: Lecture Notes in Computational Science and Engineering 58. Springer, Berlin - Heidelberg - New York. 2008. V. XXIV. 340 p.
14. Аппалонов А.М., Масленникова Ю.С. Нейросетевое прогнозирование динамики экваториальной аномалии по данным полного электронного содержания ионосферы. Техника радиосвязи. 2021. № 3(50). С. 29–42. DOI: 10.33286/2075-8693-2021-50-29-42.
15. Heelis R.A. Electrodynamics in the low and middle latitude ionosphere: A tutorial. J. Atmos. Solar-Terr. Phys. 2004. № 66. Р. 825–838. DOI: 10.1016/j.jastp.2004.01.034.
16. Jelektronnyj resurs: laboratorija DRAO. URL: https://nrc.canada.ca/en/research-development/nrc-facilities/dominion-radioastro-physical-observatory-research-facility (data obrashhenija: 15.06.2023) (in Russian).
17. Taylor H., et al. RMCSat: An F10. 7 Solar Flux Index CubeSat Mission. Remote Sensing. 2021. V. 13. № 23. Р. 4754. DOI: 10.3390/rs13234754.
18. Jelektronnyj resurs: SPACE WEATHER PREDICTION CENTER. URL: https://www.swpc.noaa.gov/phenomena/f107-cm-radio-emissions (data obrashhenija: 15.06.2023) (in Russian).
19. Rajesh Vaishnav, Christoph Jacobi, Jens Berdermann, Erik Schmölter, Mihail Codrescu. Delayed ionospheric response to solar extreme ultraviolet radiation variations: A modeling approach. Advances in Space Research. 2022. V. 69. № 6. Р. 2460-2476. DOI: 10.1016/j.asr.2021.12.041.
20. Klimenko V.V., Klimenko M.V., Bessarab F.S., Sukhodolov T.V., Koren’kov Yu.N., Funke B., Rozanov E.V. Global EAGLE Model as a tool for studying the influence of the atmosphere on the electric field in the equatorial ionosphere. Russian Journal of Physical Chemistry B. 2019. V. 13. Р. 720-726. DOI: 10.1134/S1990793119040079.
21. Karlov A.M., Volhonskaja E.V., Rjabec A.Ja. Opredelenie kovariacionnoj matricy oshibok fil'tracii pri otklonenii parametrov ustrojstv formirovanija i obrabotki signala ot optimal'nyh znachenij. Radiotehnika. 2019. № 2. S. 12–16. DOI: 10.18127/j00338486-201902-02 (in Russian).