Abstract
AbstractStatistical wave models, describing the distribution of wave amplitudes as a function of location, geomagnetic activity, and other parameters, are needed as the basis to describe the wave‐particle interactions within numerical models of the radiation belts. In this study, we widen the scope of the statistical wave models by investigating which of the solar wind parameters or geomagnetic indices and their time lags have the greatest influence on the amplitudes of lower band chorus (LBC) waves in the inner magnetosphere. The solar wind parameters or geomagnetic indices with the greatest control over the waves were found using the error reduction ratio (ERR) analysis, which plays a key role in system identification modeling techniques. In this application, the LBC magnitudes at different locations are considered as the output data, while the lagged solar wind parameters are the input data. The ERR analysis automatically determines a set of the most influential parameters that explain the variations in the emissions. Both linear and nonlinear applications of the ERR analysis are compared using solar wind inputs and show that the linear ERR analysis can be misleading. The linear results show that the interplanetary magnetic field (IMF) factor has the most influence on at each magnetic local time (MLT) sector. However, the nonlinear ERR analysis shows that the IMF factor coupled with the solar wind velocity has the main contribution to the LBC wave magnitudes. When geomagnetic indices are included as inputs with the solar wind parameters to the nonlinear ERR analysis, the results show that the majority of the variation in emissions may be attributed to the Auroral Electrojet (AE) index. In the dawn sectors between 00 and 12 MLT and 5 < L < 7, the AE index multiplied by the solar wind velocity with zero time lag has the most influence on the amplitudes of LBC. For 5 < L < 7, the parameters with the highest ERR are the AE index multiplied by the solar wind velocity with a 2‐hr time lag at 12–16 MLT, the linear AE index with a 2‐hr time lag at 16–20 MLT, and AE index multiplied by the IMF factor with zero lag at 20–00 MLT. For 4 < L < 5, the parameters with the highest ERR are the AE index multiplied by the solar wind dynamic pressure with zero time lag at 00–04 MLT, the AE index multiplied by the solar wind velocity with zero time lag between 14 and 12 MLT, the AE index multiplied by the solar wind velocity with a 2‐hr time lag at 12–16 MLT, the Dst index with a 6‐hr time lag at 12–16 MLT, and the AE index multiplied by the IMF factor with zero lag at 20–00 MLT.
Highlights
Energetic electrons were observed by Van Allen (1959) during the first in situ space radiation measurements, leading to the discovery of the radiation belts
When geomagnetic indices are included as inputs with the solar wind parameters to the nonlinear error reduction ratio (ERR) analysis, the results show that the majority of the variation in emissions may be attributed to the Auroral Electrojet (AE) index
For 4 < L < 5, the parameters with the highest ERR are the AE index multiplied by the solar wind dynamic pressure with zero time lag at 00–04 magnetic local time (MLT), the AE index multiplied by the solar wind velocity with zero time lag between 14 and 12 MLT, the AE index multiplied by the solar wind velocity with a 2-hr time lag at 12–16 MLT, the Dst index with a 6-hr time lag at 12–16 MLT, and the AE index multiplied by the interplanetary magnetic field (IMF) factor with zero lag at 20–00 MLT
Summary
Energetic electrons were observed by Van Allen (1959) during the first in situ space radiation measurements, leading to the discovery of the radiation belts. Electrons with energies from 1 to 100 keV can cause surface charging that interferes with the satellite electronic systems (Mullen et al, 1986; Olsen, 1983), while electrons with energies around 1 MeV and above can cause deep dielectric charging that may permanently damage the materials onboard the satellite (Baker et al, 1987; Gubby & Evans, 2002; Lohmeyer & Cahoy, 2013; Lohmeyer et al, 2015; Wrenn et al, 2002) These problems can range from single event upsets, from which the spacecraft will recover, to the total failure of the satellite (Blake et al, 1992). With prior warning of when these high fluences are expected to occur, it is possible for satellite operators to mitigate some of the damaging effects of these electrons
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