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Auroral precipitation is the second major energy source after solar irradiation that ionizes the Earth’s upper atmosphere. Diffuse electron aurora caused by wave-particle interaction in the inner magnetosphere (L < 8) takes over 60% of total auroral energy flux, strongly contributing to the ionospheric conductance and thus to the ionosphere-thermosphere dynamics. This paper quantifies the impact of chorus waves on the diffuse aurora and the ionospheric conductance during quiet, medium, and strong geomagnetic activities, parameterized by AE <100, 100 < AE < 300, and AE > 300, respectively. Using chorus wave statistics and inner-magnetosphere plasma conditions from Timed History Events and Macroscale Interactions during Substorms (THEMIS) observations, we directly derive the energy spectrum of diffuse electron precipitation under quasi-linear theory. We then calculate the height-integrated conductance from the wave-driven aurora spectrum using the electron impact ionization model of Fang et al. (Geophys. Res. Lett., 2010, 37) and the MSIS atmosphere model. By utilizing Fang’s ionization model, the US Naval Research Laboratory Mass Spectrometer and Incoherent Scattar Radar (NRLMSISE-00) model from 2000s for the neutral atmosphere components, and the University of California, Los Angeles (UCLA) Full Diffusion Code, we improve upon the standard generalization of Maxwellian diffuse electron precipitation patterns and their resulting ionosphere conductance. Our study of global auroral precipitation and ionospheric conductance from chorus wave statistics is the first statistical model of its kind. We show that the total electron flux and conductance pattern from our results agree with those of Ovation Prime model over the pre-midnight to post-dawn sector as geomagnetic activity increases. Our study examines the relative contributions of upper band chorus (UBC) and lower band chorus wave (LBC) driven conductance in the ionosphere. We found LBC waves drove diffuse electron precipitation significantly more than UBC waves, however it is possible that THEMIS data may have underestimated the upper chorus band wave observations for magnetic latitudes below 65 $°$. 

Magnetospheric precipitation plays an important role for the coupling of Magnetosphere, Ionosphere, and Thermosphere (M-I-T) systems. Particles from different origins could be energized through various physical mechanisms and in turn disturb the Ionosphere, the ionized region of the Earth’s atmosphere that is important for telecommunication and spacecraft operations. Known to cause aurora, bright displays of light across the night sky, magnetospheric particle precipitation, modifies ionospheric conductance further affecting the plasma convection, field-aligned (FAC) and ionospheric currents, and ionospheric/thermospheric temperature and densities. Therefore, understanding the properties of different sources of magnetospheric precipitation and their relative roles on electrodynamic coupling of M-I across a broad range of spatiotemporal scales is crucial. In this paper, we detail some of the important open questions regarding the origins of magnetospheric particle precipitation and how precipitation affects ionospheric conductance. In a companion paper titled “The Significance of Magnetospheric Precipitation for the Coupling of Magnetosphere-Ionosphere-Thermosphere Systems: Effects on Ionospheric Conductance”, we describe how particle precipitation affects the vertical structure of the ionospheric conductivity and provide recommendations to improve its modelling. 

Abstract We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020,https://doi.org/10.1029/2020JA027908), the FAC‐derived auroral conductance model of Robinson et al. (2020,https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993,https://doi.org/10.1029/92gl02109). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (Φ_PC) from ML‐AIM, the Weimer (2005,https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005,https://doi.org/10.1029/2004ja010884) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times. 

Abstract Foreshock transient (FT) events are frequently observed phenomena that are generated by discontinuities in the solar wind. These transient events are known to trigger global‐scale magnetic field perturbations (e.g., ULF waves). We report a series of FT events observed by the Magnetospheric Multiscale mission in the upstream bow shock region under quiet solar wind conditions. During the event, ground magnetometers observed significant Pc1 wave activity as well as magnetic impulse events in both hemispheres. Ground Pc1 wave observations show ∼8 min time delay (with some time differences) from each FT event which is observed at the bow shock. We also find that the ground Pc1 waves are observed earlier in the northern hemisphere compared to the southern hemisphere. The observation time difference between the hemispheres implies that the source region of the wave is the off‐equatorial region. 

Abstract Mesoscale high‐latitude electric fields are known to deposit energy into the ionospheric and thermospheric system, yet the energy deposition process is not fully understood. We conduct a case study to quantify the energy deposition from mesoscale high‐latitude electric fields to the thermosphere. For the investigation, we obtain the high‐latitude electric field with mesoscale variabilities from Poker Flat Incoherent Scatter Radar measurements during a moderate geomagnetic storm, providing the driver for the Global Ionosphere and Thermosphere Model (GITM) via the High‐latitude Input for Mesoscale Electrodynamics framework. The HIME‐GITM simulation is compared with GITM simulations driven by the large‐scale electric field from the Weimer model. Our modeling results indicate that the mesoscale electric field modifies the thermospheric energy budget primarily through enhancing the Joule heating. Specifically, in the local high‐latitude region of interest, the mesoscale electric field enhances the Joule heating by up to five times. The resulting neutral temperature enhancement can reach up to 50 K above 200 km altitude. Significant increase in the neutral density above 250 km altitude and in the neutral wind speed are found in the local region as well, lagging a few minutes after the Joule heating enhancement. We demonstrate that the energy deposited by the mesoscale electric field transfers primarily to the gravitational potential energy in the thermosphere.