JpGU-AGU Joint Meeting 2020

Presentation information

[J] Oral

M (Multidisciplinary and Interdisciplinary) » M-IS Intersection

[M-IS20] Atmospheric Electricity: Lightning discharges and related physical phenomena

convener:Yasuhide Hobara(Graduate School of Information and Engineering Department of Communication Engineering and Informatics, The University of Electro-Communications)

[MIS20-11] Prediction of Spatio-temporal Variation for Electron Flux at Geostationary Orbits

*Hiroki Nomoto1, Yasuhide Hobara1, Hiroshi Kikuchi1, Hayato Iibuchi1, Michael A Balikhin2 (1.The University of Electro-Communications, 2.The University of Sheffield)

Keywords:Geostationary orbit, Electron flux

In this paper, we present the prediction model of energetic electrons (> 2 MeV) simultaneously at multi-points in the terrestrial radiation belt. Our model was based upon the nonlinear system identification technique so-called Multi-Input Single-Output NARMAX (Nonlinear Autoregressive Moving Average with Exogenous)[1-2]. To predict spatio-temporal dependences of the electron flux, we have used high latitude Pc5 ULF data at 8 different MLTs as external local forcing parameters and used global parameters such as solar wind parameters (solar wind velocity and pressure, IMF) and SYM/H. We have constructed the prediction model with 24-hour ahead prediction with 3-hour time resolution at 8 different MLTs. As a result, the model has high prediction performance. In addition, Pc5 ULF data contributes to improve the accuracy at some MLT indicative of local dependence of the electron flux. Figure 1 shows an example snap shot of spatial variation of the predicted electron flux. Correlation coefficient between observed and predicted flux at MLT near GOES15 is > 90%. We also have examined the correlation coefficient for the MLT without satellite data being available by comparing the prediction results at GOES13 (60°W) based upon the GOES15 (135°W) with sampled data from GOES13, the correlation coefficient is still found to be high ~ 70%. Moreover, the effect from geomagnetic storm on the prediction accuracy will be discussed.