ABSTRACT
The difficulty of prediction of the time series of relativistic electrons flux in the outer radiation belt of the Earth is caused by the complexity and nonlinearity of the magnetosphere of the Earth as a dynamic system, and by the properties of data obtained from space experiments. This study considers different approaches to neural network prediction of the values of relativistic electrons flux in the outer radiation belt of the Earth by the parameters of solar wind measured at the Earth's orbit and by the values of geomagnetic indices. Comparison of quality indices of predictions with horizon from one to twelve hours among each other and with predictions of trivial models is performed.
- Advanced Composition Explorer (ACE) Project, http://www.srl.caltech.edu/ACE/Google Scholar
- Baker, D.N., McPherron, R.L., Cayton, T.E., and Klebesadel, R.W. 1990. Linear prediction filter analysis of relativistic electron properties at 6.6 RE. J. Geophys. Res. 95 (A9), 15133--15140. DOI= http://dx.doi.org/10.1029/JA095iA09p15133Google Scholar
- Friedel, R.H., Reeves W.G.P., and Obara, T. 2002. Relativistic electron dynamics in the inner magnetosphere -- A review. J. Atmos. Sol.-Terr. Phy. 64, 265--283. DOI= http://dx.doi.org/10.1016/S1364-6826(01)00088-8Google ScholarCross Ref
- Fukata, M., Taguchi, S., Okuzawa, T., and Obara, T. 2002. Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms. Ann. Geophys. 20 (7), 947--951. DOI= http://dx.doi.org/10.5194/angeo-20-947-2002Google ScholarCross Ref
- Geomagnetic Data Service in Kyoto, http://wdc.kugi.kyoto-u.ac.jp/wdc/Sec3.htmlGoogle Scholar
- Geostationary Operational Environmental Satellite Project, http://rsd.gsfc.nasa.gov/goes/Google Scholar
- Geostationary Operational Environmental Satellite Project in Space Weather Prediction Center, http://www.swpc.noaa.gov/ftpdir/lists/pchan/READMEGoogle Scholar
- Iucci, N., Levitin, A.E., Belov, A.V. et al. 2005. Space weather conditions and spacecraft anomalies in different orbits. Adv. Space Res. (Space Weather) 3 (1), S01001. DOI= http://dx.doi.org/10.1029/2003SW000056Google Scholar
- Kataoka, R. and Miyoshi, Y. 2008. Average profiles of the solar wind and outer radiation belt during the extreme flux enhancement of relativistic electrons at geosynchronous orbit. Ann. Geophys. 26, 1335--1339. DOI= http://dx.doi.org/10.5194/angeo-26-1335-2008Google ScholarCross Ref
- Koons, H.C. and Gorney, D.J. 1990. A neural network model of the relativistic electron flux at geosynchronous orbit. J. Geophys. Res. 96, 5549--5556. DOI= http://dx.doi.org/10.1029/90JA02380Google ScholarCross Ref
- Kuznetsov, S.N. and Tverskaya, L.V. 2007. Physical Conditions in Open Space: Radiation. In: Model of Cosmos, Panasyuk, M.I. and Novikov L.S. (eds.), Vol. 1, chapter 3.4, 518--546. Universitet, Knizhnyi dom, Moscow. (In Russian.) ISBN= 978-5-98227-419-9.Google Scholar
- Ling, A. G., Ginet, G. P., Hilmer, R. V., and Perry, K. L. 2010. A neural network-based geosynchronous relativistic electron flux forecasting model. Adv. Space Res. (Space Weather) 8 (9), S09003. DOI= http://dx.doi.org/10.1029/2010SW000576Google Scholar
- Pauliukas, G.A. and Blake, J.B. 1979. Effects of the solar wind on magnetospheric dynamics: Energetic electrons at the synchronous orbit. In: Quantitative Modeling of Magnetospheric Processes, Olson, W.P. (ed.). Geophys. Monogr. Ser. AGU, Washington D.C. 21, 180--202.Google Scholar
- Peters, E. 1994. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Wiley, 336 pp. ISBN= 978-0-471-58524-4.Google Scholar
- Reeves, G.D., McAdams K.L., Friedel, R.H.W. et al. 2003. Acceleration and loss of relativistic electrons during geomagnetic storms. Geophys. Res. Lett. 30 (10), 1529. DOI= http://dx.doi.org/10.1029/2002GL016513.Google ScholarCross Ref
- Relativistic Electron Forecast Model of Space Weather Prediction Center, http://www.swpc.noaa.gov/refm/Google Scholar
- Romanova, N.V., Pilipenko, V.A., Yagova, N.V., and Belov, A.V. 2005. Statistical Correlation of the Rate of Failures on Geosynchronous Satellites with Fluxes of Energetic Electrons and Protons. Cosmic Res+ 43 (3), 179--185. DOI= http://dx.doi.org/10.1007/s10604-005-0032-6.Google Scholar
- Space Physics Interactive Data Resource -- SPIDR, http://spidr.ngdc.noaa.gov/spidr/Google Scholar
- Space Weather Prediction Center, http://www.swpc.noaa.govGoogle Scholar
- Turner, D.L., Shprits, Y., Hartinger, M., and Angelopoulos, V. 2012. Explaining sudden losses of outer radiation belt electrons during geomagnetic storms. Natural Physics 8, 208--212. DOI= http://dx.doi.org/10.1038/nphys2185.Google ScholarCross Ref
Index Terms
- Horizon of Neural Network Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth
Recommendations
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 SeriesAbstractThis study presents the results of prediction 1–3 days ahead for the daily maximum of hourly average values of relativistic electrons flux (E > 2 MeV) in the outer radiation belt of the Earth. The input physical variables were geomagnetic indexes, ...
Practical horizon plane for low earth orbiting (LEO) satellite ground stations
TELE-INFO'09: Proceedings of the 8th Wseas international conference on Telecommunications and informaticsCommunication via satellite begins when the satellite is positioned in the desired orbital position. Ground stations can communicate with LEO (Low Earth Orbiting) satellites only when the satellite is in their visibility region. The duration of the ...
Practical horizon plane and communication duration for low earth orbiting (LEO) satellite ground stations
Communication via satellite begins when the satellite is positioned in the desired orbital position. Ground stations can communicate with LEO (Low Earth Orbiting) satellites only when the satellite is in their visibility region. The visibility region is ...
Comments