Determining the Timing of Driver Influences on 1.8–3.5 MeV Electron Flux at Geosynchronous Orbit Using ARMAX Methodology and Stepwise Regression

Abstract

The response of high energy (MeV) electron flux at geosynchronous orbit to various solar wind, interplanetary magnetic field (IMF), and magnetospheric parameters has been well studied at a daily cadence (e.g.,

Comparing Influences of Solar Wind, ULF Waves, and Substorms on 20 eV–2 MeV Electron Flux (RBSP) Using ARMAX Models

“…The ULF influence is seen mostly in the 1–2 MeV range. In previous work, ULF waves have been found to be a stronger influence if only storm recovery time periods are considered (at ∼L‐shell 6) as long quiet periods might tend to “wash out” the influence of parameters that are only rarely strong enough to be active (Simms, Engebretson, & Reeves, 2023). For MeV electrons, which rise after geomagnetic storms, it is possible to study just the after‐storm response with a reasonable sample size (i.e., more than just one or a few storms) (Simms et al., 2014), but lower energy electrons do not show stronger responses to drivers during storms (Simms, Ganushkina, et al., 2022), so there are no easily identifiable points at which responses can be studied.…”

“…These include solar wind velocity (V) (Kellerman & Shprits, 2012; Li et al., 2001, 2005; Paulikas & Blake, 1979), and number density (N) (Balikhin et al., 2011; Lyatsky & Khazanov, 2008). However, the influence of these possible drivers on MeV electron flux may not be direct, but mediated by electromagnetic waves (ULF: ultralow frequency and VLF: very low frequency) and by the electron injections of substorms (Simms, Engebretson, & Reeves, 2023; Simms et al., 2014, 2016; Simms, Engebretson, Clilverd, Rodger, Lessard, et al., 2018). Depending on the situation, higher ULF wave activity is thought to both decrease electron flux levels through outward radial diffusion (Katsavrias et al., 2015; Kellerman & Shprits, 2012; Loto'aniu et al., 2010; K. R. Mann et al., 2012; Ozeke et al., 2020; Turner et al., 2012) or enhance levels through inward radial diffusion (Hao et al., 2019; Katsavrias et al., 2019; I. Mann et al., 2004).…”

“…In addition, autocorrelation in the response variable (electron flux) can increase the apparent statistical significance of correlations. ARMAX models (autoregressive, moving average transfer functions) can address both these problems and are a better choice for analyzing the associations between time series variables (Balikhin et al., 2011; Boynton et al., 2011, 2013, 2015; Sakaguchi et al., 2015; Simms, Engebretson, & Reeves, 2022; Simms, Engebretson, & Reeves, 2023; Simms et al., 2019; Simms, Ganushkina, et al., 2022). The time series behavior of the response variable can be partitioned out by the use of AR (autoregressive) and MA (moving average terms of the error).…”

“…Using ARMAX models, we have previously explored the influence of parameters on hourly averaged geosynchronous orbit (L‐shell 6.6) 40–150 keV and 1.8–3.5 MeV electron flux (Simms, Engebretson, & Reeves, 2022; Simms, Engebretson, & Reeves, 2023; Simms, Ganushkina, et al., 2022) (GOES and LANL satellites). In the present paper, using 10 min averaged electron flux data from the van Allen probes (RBSP: Radiation Belt Storm Probes), we expand our investigation to include more L‐shells (L‐shell 2‐L‐shell 7) and a wider range of energies (20 eV–2 MeV).…”