| Abstrakt: |
We investigate the drivers of 40–150 keV hourly electron flux at geostationary orbit (GOES 13) using autoregressive moving average transfer functions (ARMAX) multiple regression models which remove the confounding effect of diurnal cyclicity and allow assessment of each parameter independently. By taking logs of the variables, we create nonlinear models. While many factors show high correlation with flux in single variable analysis (substorms, ULF waves, solar wind velocity (V), pressure (P), number density (N) and electric field (Ey), IMF Bz, Kp, and SymH), ARMAX models show substorms are the dominant influence at 40–75 keV and over 20–12 MLT, with little difference seen between disturbed and quiet periods. The Ey influence is positive post‐midnight, negative post‐noon. Pressure shows a negative influence, strongest at 150 keV. ULF waves are a more modest influence than suggested by single variable correlation. Kp and SymH show little effect when other variables are included. Using path analysis, we calculate the summed direct and indirect influences through the driving of intermediate parameters. Pressure shows a summed direct and indirect influence nearly half that of the direct substorm effect. N, V, and Bz, as indirect drivers, are equally influential. While simple correlation or neural networks can be used for flux prediction, neither can effectively identify drivers. Instead, consideration of physical influences, removing cycles that artificially inflate correlations, and controlling the effects of other parameters gives a clearer picture of which are most influential in this system. Plain Language Summary: Satellites may experience damaging surface charging due to high energy electrons present in the radiation belts. In this study, we explore the various factors that may influence these electron populations. We use an autoregressive moving average transfer function (ARMAX) statistical model that removes the confounding effect of diurnal cyclicity and allows assessment of each variable independently of others. Substorms, which inject electrons into the magnetosphere, are found to be the strongest influence, with most of their effect seen near local midnight. The electric field and pressure of the solar wind also show moderate effects. Not all variables that show high single variable correlations retain this influence in multivariate analyses. Kp and SymH, two indices of geomagnetic activity are highly correlated with electron levels in the magnetosphere, but show little influence in models controlling for the effects of solar wind parameters. Identifying direct, physical drivers, removing cycles that artificially inflate correlations, and controlling the effects of other parameters using multiple regression (specifically, ARMAX) gives a clearer picture of which parameters are most influential in this system. Key Points: Substorms, as measured by AE, are the strongest direct influence on 40–150 keV electron fluxOf the possible indirect drivers N, V, Bz show fairly equal influence on fluxAn autoregressive moving average transfer functions model removes diurnal cyclicity and allows a more accurate assessment of the correlations [ABSTRACT FROM AUTHOR] |
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