Dependence of Electron Flux Dropouts in the Earth's Outer Radiation Belt on Energy and Driving Parameters During Geomagnetic Storms

Abstract

Using 5‐year of measurements from Van Allen Probes, we present a survey of the statistical dependence of the Earth's outer radiation belt electron flux dropouts during geomagnetic storms on electron energy and various driving parameters including interplanetary magnetic field Bz, PSW, SYM‐H, and AE. By systematically investigating the dropouts over energies of 1 keV–10 MeV at L‐shells spanning 4.0–6.5, we find that the dropouts are naturally divided into three regions. The dropouts show much higher occurrence rates at energies below ∼100 keV and above ∼1 MeV compared to much smaller occurrence rate at intermediate energies around hundreds of keV. The flux decays more dramatically at energies above ∼1 MeV compared to the energies below ∼100 keV. The flux dropouts of electrons below ∼100 keV strongly depend on magnetic local time (MLT), which demonstrate high occurrence rates on the nightside (18–06 MLT), with the highest occurrence rate associated with northward Bz, strong PSW and SYM‐H, and weak AE conditions. The strongest flux decay of these dropouts is found on the nightside, which strongly depends on PSW and SYM‐H. However, there is no clear MLT dependence of the occurrence rate of relativistic electron flux dropouts above ∼1 MeV, but the flux decay of these dropouts is more significant on the dayside, with stronger decay associated with southward IMF Bz, strong PSW, SYM‐H, and AE conditions. Our statistical results are crucial for understanding of the fundamental physical mechanisms that control the outer belt electron dynamics and developing future potential radiation belt forecasting capability.

The effect of continuous geomagnetic storms on enhancements of ultrarelativistic electrons in the Earth’s outer radiation belt

“…And, magnetospheric compressions can also lead to the anisotropic distributions of ions and electrons, which generate EMIC waves on the dayside (e.g., Anderson and Hamilton, 1993;McCollough et al, 2010;Usanova et al, 2012;Zhang et al, 2016b;Saikin et al, 2016;Xue et al, 2023;Yan et al, 2023) that can cause the loss of relativistic and ultrarelativistic electrons (e.g., Zhang et al, 2016a;Su et al, 2017;Zhu et al, 2020). Hua et al (2023) found that the most significant flux losses of >1 MeV electrons occurred during the strong solar wind dynamic pressure while the flux losses barely occurred during the weak solar wind dynamic pressure. During the second geomagnetic storm of the three continuous geomagnetic storm events in this study, the solar wind dynamic pressures were relatively low or had only an instantaneous enhancement.…”

Machine‐Learning Based Identification of the Critical Driving Factors Controlling Storm‐Time Outer Radiation Belt Electron Flux Dropouts

“…Numerous SVM tutorials are available in the literature (e.g., Burges, 1998;Chang & Lin, 2011;Cortes & Vapnik, 1995;Melgani & Bruzzone, 2004). Considering that the dropout data set is a relatively small data set with tens to hundreds of dropout samples at a given L-shell and energy (Hua et al, 2023), and the time series of various driving parameters will be used as inputs (i.e., a high-dimensional problem), SVM is particularly appealing for dropout predictions.…”

“…Recent statistical studies reported the important impact of high solar wind dynamic pressure (P SW ) and southward interplanetary magnetic field (IMF) B z on producing significant relativistic electron dropouts (Gao et al, 2015;Gokani et al, 2022;Hua et al, 2023;Onsager et al, 2007;Yuan & Zong, 2013). Moreover, Hua et al (2023) suggested that dropouts strongly depend on storm (SYM-H index) and substorm (AE index) conditions. The SYM-H index, equivalent to Dst index, measures the ring current intensity near Earth (Wanliss & • We investigate the critical driving factors controlling dropouts by constructing dropout prediction models using Support Vector Machines (SVMs)…”

“…There are two major loss processes driving outer belt electron flux dropouts: electrons are either lost through the magnetopause into the interplanetary space due to the magnetopause shadowing with enhanced outward radial diffusion (Bortnik et al, 2006;Ma et al, 2020), or lost to the upper atmosphere via interacting with various plasma waves (Blum & Breneman, 2020;Drozdov et al, 2022;Lyu et al, 2022;Usanova et al, 2014). Recent statistical studies reported the important impact of high solar wind dynamic pressure (P SW ) and southward interplanetary magnetic field (IMF) B z on producing significant relativistic electron dropouts (Gao et al, 2015;Gokani et al, 2022;Hua et al, 2023;Onsager et al, 2007;Yuan & Zong, 2013). Moreover, Hua et al (2023) suggested that dropouts strongly depend on storm (SYM-H index) and substorm (AE index) conditions.…”

“…Since these nonlinearly correlated driving factors and their nonlinear interactions produce dropouts, to unravel these nonlinear correlations between various driving factors and dropouts, we take advantage of machine-learning techniques that can associate inputs with outputs in a nonlinear way, which have been widely used in space weather modeling and forecasting (Bortnik et al, 2016(Bortnik et al, , 2018Chu et al, 2017;Ma et al, 2022;Wing et al, 2022), and in discovering the important underlying/missing physical processes (Camporeale et al, 2022;Ma et al, 2023Ma et al, , 2024. In this letter, we employ Support Vector Machines (SVMs) to construct storm-time electron dropout prediction models over L = 4.0-6.0 using dropout data set based on 5-year Van Allen Probes observations from our previous study (Hua et al, 2023). The SVM models take a time history of different driving factors as inputs.…”