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We analyzed the contribution of electromagnetic ion cyclotron (EMIC) wave driven electron loss to a flux dropout event in September 2017. The evolution of electron phase space density (PSD) through the dropout showed the formation of a radially peaked PSD profile as electrons were lost at highL*, resembling distributions created by magnetopause shadowing. By comparing 2D Fokker Planck simulations of pitch angle diffusion to the observed change in PSD, we found that theμandKof electron loss aligned with maximum scattering rates at dropout onset. We conclude that, during this dropout event, EMIC waves produced substantial electron loss. Because pitch angle diffusion occurred on closed drift paths near the last closed drift shell, no radial PSD minimum was observed. Therefore, the radial PSD gradients resembled solely magnetopause shadowing loss, even though the local pitch angle scattering produced electron losses of several orders of magnitude of the PSD.

We develop and test an empirical model predicting ground‐based observations of ultralow frequency (ULF, 1–20 mHz) wave power across a range of frequencies, latitudes, and MLT sectors. This is parameterized by instantaneous solar wind speedv_sw, variance in proton number density var(Np), and interplanetary southward magnetic fieldB_z. A probabilistic model of ULF wave power will allow us to address uncertainty in radial diffusion coefficients and therefore improve diffusion modeling of radial transport in Earth's outer radiation belt. Our model can be used in two ways to reproduce wave power: by sampling from conditional probability distribution functions and by using the mean (expectation) values. We derive a method for testing the quality of the parameterization and test the ability of the model to reproduce ULF wave power time series. Sampling is a better method for reproducing power over an extended time period as it retains the same overall distribution, while mean values are better for predicting the power in a time series. The model predicts each hour in a time series better than the assumption that power persists from the preceding hour. Finally, we review other sources of diffusion coefficient uncertainty. Although this wave model is designed principally for the goal of improved radial diffusion coefficients to include in outer radiation belt diffusion‐based modeling, we anticipate that our model can also be used to investigate the occurrence of ULF waves throughout the magnetosphere and hence the physics of ULF wave generation and propagation.

We show that a white‐light all‐sky imager can estimate Pedersen conductance with an uncertainty of 3 mho or 40%. Using a series of case studies over a wide range of geomagnetic activity, we compare estimates of Pedersen conductance from the backscatter spectrum of the Poker Flat Incoherent Scatter Radar with auroral intensities. We limit this comparison to an area bounding the radar measurements and within a limited area close to (but off) imager zenith. We confirm a linear relationship between conductance and the square root of auroral intensity predicted from a simple theoretical approximation. Hence, we extend a previous empirical result found for green‐line emissions to the case of white‐light off‐zenith emissions. The difference between the radar conductance and the best‐fit relationship has a mean of −0.76 ± 4.8 mho and a relative mean difference of 21% ± 78%. The uncertainties are reduced to −0.72 ± 3.3 mho and 0% ± 40% by averaging conductance over 10 min, which we attribute to the time that auroral features take to move across the imager field being greater than the 1‐min resolution of the radar data. Our results demonstrate and calibrate the use of Time History of Events and Macroscale Interactions during Substorms all‐sky imagers for estimating Pedersen conductance. This technique allows the extension of estimates of Pedersen conductance from Incoherent Scatter Radars to derive continental‐scale estimates on scales of ~1–10 min and ~100 km². It thus complements estimates from low‐altitude satellites, satellite auroral imagers, and ground‐based magnetometers.

Substorms are a highly variable process, which can occur as an isolated event or as part of a sequence of multiple substorms (compound substorms). In this study we identify how the low‐energy population of the ring current and subsequent energization varies for isolated substorms compared to the first substorm of a compound event. Using observations of H⁺and O⁺ions (1 eV to 50 keV) from the Helium Oxygen Proton Electron instrument onboard Van Allen Probe A, we determine the energy content of the ring current in L‐MLT space. We observe that the ring current energy content is significantly enhanced during compound substorms as compared to isolated substorms by ∼20–30%. Furthermore, we observe a significantly larger magnitude of energization (by ∼40–50%) following the onset of compound substorms relative to isolated substorms. Analysis suggests that the differences predominantly arise due to a sustained enhancement in dayside driving associated with compound substorms compared to isolated substorms. The strong solar wind driving prior to onset results in important differences in the time history of the magnetosphere, generating significantly different ring current conditions and responses to substorms. The observations reveal information about the substorm injected population and the transport of the plasma in the inner magnetosphere.