Abstract
Abstract. During the recovery phase of geomagnetic storms, the flux of relativistic (>2 MeV) electrons at geosynchronous orbits is enhanced. This enhancement reaches a level that can cause devastating damage to instruments on satellites. To predict these temporal variations, we have developed neural network models that predict the flux for the period 1–12 h ahead. The electron-flux data obtained during storms, from the Space Environment Monitor on board a Geostationary Meteorological Satellite, were used to construct the model. Various combinations of the input parameters AL, SAL, Dst and SDst were tested (where S denotes the summation from the time of the minimum Dst). It was found that the model, including SAL as one of the input parameters, can provide some measure of relativistic electron-flux prediction at geosynchronous orbit during the recovery phase. We suggest from this result that the relativistic electron-flux enhancement during the recovery phase is associated with recurring substorms after Dst minimum and their accumulation effect.Key words. Magnetospheric physics (energetic particles, trapped; magnetospheric configuration and dynamics; storms and substorms)
Highlights
During the recovery phase of geomagnetic storms, the flux of relativistic (>2 MeV) electrons at geostationary orbits (GEO) is enhanced
We show artificial neural networks (ANN) modelling of the relativistic-electron flux for the recovery phase is possible using AL, and we suggest the significance of recurring substorms in the flux enhancement
The substorm effect represented by AL has a significance influence on the flux enhancement in the recovery phase
Summary
High flux levels of relativistic electrons can cause irreparable damage to the instruments on satellites (Gussenhoven et al, 1991; Baker et al, 1997), and this practical consequence of the flux enhancement has promoted the development of empirical models. Nagai (1988) designed a linear prediction filter for the prediction of daily averages of electron flux at GEO, using the Kp index as an input. Nagai (1988) designed a linear prediction filter for the prediction of daily averages of electron flux at GEO, using the Kp index as an input. This model predicted successfully the electron flux on a daily scale. Modeling using ANN has predicted successfully the energetic-electron flux with a time resolution of 1 h (Stringer et al, 1996; Freeman et al, 1998). Freeman et al (1998) used the current low-energy (35 keV) electron flux, plus those existing 15 min and 75 min earlier, to predict the energetic (100 keV to 1.5 MeV) electron flux for the storm of 3–4 November 1993. We show ANN modelling of the relativistic-electron flux for the recovery phase is possible using AL (the summation of AL from the time of Dst minimum in the main phase), and we suggest the significance of recurring substorms in the flux enhancement
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