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Medium-Term Prediction of Relativistic Electron Fluxes in a Geostationary Orbit Using Machine Learning Methods Based on Observations of Solar Coronal Holes

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Abstract

The paper proposes a model for predicting the integral daily fluxes (fluences) of relativistic electrons (RE) (E > 2 MeV) of the Earth's outer radiation belt in a geostationary orbit using images of the Sun in the ultraviolet range. The results show that the accuracy of the forecast of the RE fluxes three or four days ahead increases significantly when adding the predicted values of solar wind speed at the Earth’s orbit, obtained by processing images of the Sun in the UV range from AIA instrument, SDO Observatory, to the input parameters of the forecasting model.

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Funding

The work was supported by the Russian Science Foundation, project no. 16-17-00098-P.

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Authors and Affiliations

  1. Skobeltsyn Institute of Nuclear Physics, Moscow State University, 119991, Moscow, Russia

    I. N. Myagkova, Yu. S. Shugai, V. V. Kalegaev, V. A. Kolmogorova & S. A. Dolenko

Authors
  1. I. N. Myagkova
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  2. Yu. S. Shugai
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  3. V. V. Kalegaev
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  4. V. A. Kolmogorova
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  5. S. A. Dolenko
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Correspondence to I. N. Myagkova.

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Translated by A. Nikol’skii

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Myagkova, I.N., Shugai, Y.S., Kalegaev, V.V. et al. Medium-Term Prediction of Relativistic Electron Fluxes in a Geostationary Orbit Using Machine Learning Methods Based on Observations of Solar Coronal Holes. Geomagn. Aeron. 60, 279–288 (2020). https://doi.org/10.1134/S0016793220030123

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