Chinese Journal of Space Science · 2013 · 11 citations · 8 references
Natural SciencesEarth SciencesPhysicsAtmospheric ElectricityGeomagnetismPlasma PhysicsPlanetary MagnetospheresMagnetospheric Modeling
Geosynchronous Earth Orbit (GEO) is in the earth's outer radiation belts, where energetic electrons are enriched. The flux of energetic electron will surge after the geomagnetic disturbance, which is one of the causes for the failure of satellites. So, it is important to predict the electron flux on the geosynchronous orbit in order to prevent the satellite from harming. As the enhancement of electron flux is controlled by many conditions and its temporal and spatial evolution is complex, it needs to analyze various data to make the prediction model. In this work, energetic flux data from GOES-12 was statistically analyzed together with geomagnetic <em>A<sub>p</sub></em> index and solar wind velocity and combination of input parameters was determined. An artificial neural network based model was built and trained with the data mentioned above. The prediction of electron flux of <em>E</em> >2MeV one day ahead has been made and the result was good that the prediction efficiencies were 0.766, 0.808 and 0.882 for the year of 2008, 2009 and 2010, which is a little better than other models. The prediction for electron flux of FY-2D satellite was made applying the GOES-12 prediction result by analyzing the flux related characteristics. The statistics showed that the <em>E</em> >2MeV electron flux measurement of the two kinds of satellite become similar when the flux was above 10<sup>8</sup> cm<sup>-2</sup>·d<sup>-1</sup>·sr<sup>-1</sup>. Because the deep charge effect become significant when <em>E</em> >2MeV electron flux exceed 10<sup>8</sup> cm<sup>-2</sup>·d<sup>-1</sup>·sr<sup>-1</sup>, the energetic electron alert for FY-2D satellite was made with the result by GOES prediction and got good result.
8
Xinlin Li, M. Temerin · Space Science Reviews · 2001 · 168 citations
A neural network-based geosynchronous relativistic electron flux forecasting model
A. G. Ling, G. P. Ginet, R. V. Hilmer et al. · Space Weather · 2010 · 82 citations · Full text