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Abstract Electron fluxes (20 eV–2 MeV, RBSP‐A satellite) show reasonable simple correlation with a variety of parameters (solar wind, IMF, substorms, ultralow frequency (ULF) waves, geomagnetic indices) over L‐shells 2–6. Removing correlation‐inflating common cycles and trends (using autoregressive and moving average terms in an ARMAX analysis) results in a 10 times reduction in apparent association between drivers and electron flux, although many are still statistically significant (p < 0.05). Corrected influences are highest in the 20 eV–1 keV and 1–2 MeV electrons, more modest in the midrange (2–40 keV). Solar wind velocity and pressure (but not number density), IMF magnitude (with lower influence ofB_z), SME (a substorm measure), a ULF wave index, and geomagnetic indices Kp and SymH all show statistically significant associations with electron flux in the corrected individual ARMAX analyses. We postulate that only pressure, ULF waves, and substorms are direct drivers of electron flux and compare their influences in a combined analysis. SME is the strongest influence of these three, mainly in the eV and MeV electrons. ULF is most influential on the MeV electrons. Pressure shows a smaller positive influence and some indication of either magnetopause shadowing or simply compression on the eV electrons. While strictly predictive models may improve forecasting ability by including indirect driver and proxy parameters, and while these models may be made more parsimonious by choosing not to explicitly model time series behavior, our present analyses include time series variables in order to draw valid conclusions about the physical influences of exogenous parameters. 

Abstract The 400 worst‐case severe environments for surface charging detected at Los Alamos National Laboratory satellites during the years of 1990–2005 as binned by the definitions of four criteria developed by Matéo‐Vélez et al. (2018,https://doi.org/10.1002/2017sw001689) and the solar wind and Interplanetary Magnetic Field (IMF) parameters and geomagnetic activity indices are analyzed. The conducted analysis shows that only Auroral Electrojet/Auroral Lower index determines the highest risk for severe environments for surface charging to happen. The presence of a substorm with the southward turning pattern in IMFindicates that the environment can be severe for surface charging to occur but this environment will not depend on whether a substorm was moderate or intense. No clear dependence on IMFis found for risk to a severe environment to occur. Appearances of severe environments for surface charging do not necessarily require high values ofKp(Planetarische Kennziffer) and no storm is needed for such an event to be detected. Among solar wind parameters, solar wind velocityis directly related to the highest risk of severe environments, dependent on thevalue; and number densityis of no importance. Two criteria for severe environment events based on the enhancements of low energy particle fluxes exhibit clearer dependencies on the solar wind and IMF parameters and geomagnetic activity indices with more distinct patterns in their time history. 

Abstract Surface charging by keV (kiloelectron Volt) electrons can pose a serious risk for satellites. There is a need for physical models with the correct and validated dynamical behavior. The 18.5‐month (2013–2015) output from the continuous operation online in real time as a nowcast of the Inner Magnetosphere Particle Transport and Acceleration Model (IMPTAM) is compared to the GOES 13 MAGnetospheric Electron Detector (MAGED) data for 40, 75, and 150 keV energies. The observed and modeled electron fluxes were organized by Magnetic Local Time (MLT) and IMPTAM driving parameters; the observed Interplanetary Magnetic Field (IMF)B_Z,B_Y, and |B|; the solar wind speedV_SW; the dynamic pressureP_SW; andKpandSYM‐Hindices. The peaks for modeled fluxes are shifted toward midnight, but the ratio between the observed and modeled fluxes at around 06 MLT is close to 1. All the statistical patterns exhibit very similar features with the largest differences of about 1 order of magnitude at 18–24 MLT. Based on binary event analysis, 20–78% of threshold crossings are reproduced, but Heidke skill scores are low. The modeled fluxes are off by a factor of 2 in terms of the median symmetric accuracy. The direction of the error varies with energy: overprediction by 50% for 40 keV, overprediction by 2 for 75 keV, and underprediction by 18% for 150 keV. The revealed discrepancies are due to the boundary conditions developed for ions but used for electrons, absence of substorm effects, representations of electric and magnetic fields which can result in not enough adiabatic acceleration, and simple models for electron lifetimes.