student from 01.01.2020 until now
Moscow, Moscow, Russian Federation
employee from 01.01.2015 until now
Moscow, Moscow, Russian Federation
We have investigated the relationship of variations in >0.7 and >2 MeV electron fluxes of Earth's outer radiation belt in a circular polar orbit with solar wind and interplanetary magnetic field parameters, as well as with geomagnetic indices and the logarithmic electron flux in the geostationary orbit in order to explore the possibility of predicting them. We have selected the optimal input features for predicting electron fluxes in low polar orbits, which is important for ensuring the radiation safety of future space missions. We have examined integral and maximum electron fluxes of these energies over the span of a day. We have obtained forecasts with a horizon of 1 and 2 days for an interval of 2 months in 2020 for daily maximum and integral fluxes based on linear regression.
Earth’s radiation belts, relativistic electron fluxes, forecasting, machine learning, circular polar orbit
1. Alwosheel A., van Cranenburgh S., Chorus C.G. Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J. Choice Modelling. 2018, vol. 28, pp. 167–182. DOI:https://doi.org/10.1016/j.jocm.2018.07.002.
2. Baker D.N., McPherron R.L., Cayton T.E., Klebesadel R.W. Linear prediction filter analysis of relativistic electron properties at 6.6 RE. J. Geophys. Res. 1990, vol. 95, iss. A9, pp. 15133–15140. DOI:https://doi.org/10.1029/JA095iA09p15133.
3. Belov A.V., Villoresi J., Dorman L.I., et al. The influence of the space environment on the functioning of artificial Earth satellites. Geomagnetism and Aeronomy. 2004, vol. 44, iss. 4, pp. 502–510.
4. Balikhin M.A., Boynton R.J., Walker S.N., et al. Using the NARMAX approach to model the evolution of energetic electrons fluxes at geostationary orbit. Geophys. Res. Lett. 2011, vol. 38, iss. 18. DOI:https://doi.org/10.1029/2011GL048980.
5. Botek E., Pierrard V., Winant A. Prediction of radiation belts electron fluxes at a Low Earth Orbit using neural networks with PROBA-V/EPT data. Space Weather. 2023, vol. 21, iss. 7, e2023SW003466. DOI:https://doi.org/10.1029/2023SW003466.
6. Cole D.G. Space weather: Its effects and predictability. Space Sci. Rev. 2003, vol. 107, pp. 295–302. DOI:https://doi.org/10.1023/A:1025500513499.
7. Demidenko E.Z. Linear and nonlinear regression. Moscow: Finance and Statistics, 1981, 302 p
8. Denton M.H., Henderson M.G., Jordanova V.K., et al. An improved empirical model of electron and ion fluxes at geosynchronous orbit based on upstream solar wind conditions. Space Weather. 2016, vol. 14, iss. 7, pp. 511–523. DOI:https://doi.org/10.1002/2016SW001409.
9. Glauert S.A., Horne R.B., Meredith N.P. Three-dimensional electron radiation belt simulations using the BAS Radiation Belt Model with new diffusion models for chorus, plasmaspheric hiss, and lightning-generated whistlers. J. Geophys. Res.: Space Phys. 2014, vol. 119, iss. 1, pp. 268–289. DOI:https://doi.org/10.1002/2013JA019281.
10. Iucci N., Levitin A., Belov E., et al. Space weather conditions and spacecraft anomalies in different orbits. Space Weather. 2005, vol. 3, iss. 1.DOI:https://doi.org/10.1029/2003SW000056.
11. Kalegaev V., Panasyuk M., Myagkova I., et al. Monitoring, analysis and post-casting of the Earth’s particle radiation environment during February 14 – March 5, 2014. Space Weather Space Climate. 2019, vol. 9, iss. A29. DOI:https://doi.org/10.1051/swsc/2019029.
12. Kalegaev V., Kaportseva K., Myagkova I., et al. Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations. Adv. Space Res. 2023, vol. 72, iss. 12, pp. 5376–5390. DOI:https://doi.org/10.1016/j.asr.2022.08.033.
13. Kataoka R., Miyoshi Y. Average profiles of the solar wind and outer radiation belt during the extreme flux enhancement of relativistic electrons at geosynchronous orbit. Ann. Geophys. 2008, vol. 26, iss. 6, pp. 1335‒1339. DOI:https://doi.org/10.5194/angeo-26-1335-2008.
14. Koons H.C., Gorney D.J. A neural network model of the relativistic electron flux at geosynchronous orbit. J. Geophys. Res. 1991, vol. 96, iss. A4, pp. 5549–5556.DOI:https://doi.org/10.1029/90JA02380.
15. Kudela K. Space weather near Earth and energetic particles: Selected results. J. Physics Conf. Ser. 2013, vol. 409, iss. 1. DOI:https://doi.org/10.1088/1742-6596/409/1/012017.
16. Kuznetsov S.N., Myagkova I.N., Yushkov B.Yu., et al. Dynamics of the Earth's radiation belts during strong magnetic storms according to data from the CORONAS-F satellite. Astronomical Bulletin. Studies of the Solar System. 2007, vol. 41, iss. 4, pp. 369–378.
17. Landis D.A., Saikin A.A., Zhelavskaya I., et al. NARX neural network derivations of the outer boundary radiation belt electron flux. Space Weather. 2022, vol. 20, iss. 5, e2021SW002774. DOI:https://doi.org/10.1029/2021SW002774.
18. Li W., Hudson M.K. Earth’s Van Allen radiation belts: From discovery to the Van Allen Probes era. J. Geophys. Res.: Space Phys. 2019, vol. 124, iss. 11, pp. 8319–8351. DOI:https://doi.org/10.1029/2018JA025940.
19. Li X., Baker D.N., Kanekal S.G., et al. Long term measurements of radiation belts by SAMPEX and their variations. Geophys. Res. Lett. 2001, vol. 28, iss. 20, pp. 3827–3830. DOI:https://doi.org/10.1029/2001gl013586.
20. Ling A.G., Ginet G.P., Hilmer R.V., Perry K.L. A neural network-based geosynchronous relativistic electron flux forecasting model. Space Weather. 2010, vol. 8, iss. 9. DOI:https://doi.org/10.1029/2010SW000576.
21. Lyatsky W., Khazanov G.V. A predictive model for relativistic electrons at geostationary orbit. Geophys. Res. Lett. 2008, vol. 35, iss. 15, L15108. DOI:https://doi.org/10.1029/2008GL034688.
22. Myagkova I., Efitorov A., Shiroky V., Dolenko S.A. Quality of prediction of daily relativistic electrons flux at geostationary orbit by machine learning methods. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. 2019, pp. 556–565. DOI:https://doi.org/10.1007/978-3-030-30490-4_45.
23. Myagkova I.N., Shirokiy V.R., Shugai Yu.S., et al. Short- and medium-term forecasting of relativistic electron fluxes in the Earth’s outer radiation belt using machine learning methods. Meteorology and Hydrology. 2021, iss. 3, pp. 47–57. DOI:https://doi.org/10.52002/0130-2906-2021-3-47-57.
24. Novikov L.S., Voronina E.N. Interaction of Spacecraft with the Environment. Moscow: KDU, 2021, 560 p.
25. Osedlo V.I., Kalegaev V.V., Rubinshtein I.A., et al. Monitoring the radiation state of the near-Earth space on the Arktika-M No. 1 Satellite. Cosmic Res. 2022, vol. 60, iss. 6, pp. 406–419. DOI:https://doi.org/10.1134/S0010952522060089.
26. Pilipenko V., Yagova N., Romanova N., Allen J. Statistical relationships between the satellite anomalies at geostationary orbits and high-energy particles. Adv. Space Res. 2006, vol. 37, iss. 6, pp. 1192–1205. DOI:https://doi.org/10.1016/j.asr.2005.03.152.
27. Potapov A., Ryzhakova L., Tsegmed B. A new approach to predict and estimate enhancements of "killer" electron flux at geosynchronous orbit. Acta Astronaut. 2016, vol. 126, pp. 47–51. DOI:https://doi.org/10.1016/j.actaastro.2016.04.017.
28. Romanova N.V., Pilipenko V.A., Yadova N.V., Belov A.V. Statistical relationship of the frequency of failures on geostationary satellites with the fluxes of energetic electrons and protons. Space Res. 2005, vol. 43, iss. 3, pp. 186–193.
29. Son J., Moon Y.-J., Shin S. 72-hour time series forecasting of hourly relativistic electron fluxes at geostationary orbit by deep learning. Space Weather. 2022, vol. 20, iss. 10, e2022SW003153. DOI:https://doi.org/10.1029/2022sw003153.
30. Stepanova M., Pinto V., Antonova E. Regarding the relativistic electron dynamics in the outer radiation belt: a historical view. Rev. Modern Plasma Physics. 2024, vol. 8, iss. 25. DOI:https://doi.org/10.1007/s41614-024-00165-4.
31. Sun X., Lin R., Liu S., et al. Modeling the relationship of ≥2 MeV electron fluxes at different longitudes in geostationary orbit by the machine learning method. Remote Sensing. 2021, vol. 13, iss. 17, p. 3347. DOI:https://doi.org/10.3390/rs13173347.
32. Vernov S.N., Grigorov N.L., Logachev Yu.I., Chudakov A.E. Measurements of cosmic radiation on an artificial Earth satellite. Reports of the Academy of Sciences. 1958, vol. 120, iss. 6, pp. 1231–1233.
33. Wei L., Zhong Q., Lin R., et al. Quantitative prediction of high-energy electron integral flux at geostationary orbit based on deep learning. Space Weather. 2018, vol. 16, iss. 7, pp. 903–916. DOI:https://doi.org/10.1029/2018SW001829.
34. Williams D.J., Arens J.F., Lanzerotti L.J. Observations of trapped electrons at low and high altitudes. J. Geophys. Res. 1968, vol. 73, iss. 17, pp. 5673–5696.DOI:https://doi.org/10.1029/ja073i017p05673.
35. URL: https://swx.sinp.msu.ru/ (accessed September 10, 2024).
36. URL: http://www.swpc.noaa.gov/ (accessed September 10, 2024).
37. URL: https://rscf.ru/project/22-62-00048/ (accessed September 10, 2024)
