Data-driven Modeling of Equatorial Electrojet Day-to-day Variability
The Equatorial Electrojet (EEJ) results from an ionospheric dynamo driven by neutral wind-plasma interactions in a spatially varying geomagnetic field. Due to highly variable wave forcing from atmospheric tides originating in the lower atmosphere, the EEJ exhibits significant day-to-day variability. The EEJ can be quantified by magnetic field perturbations measured near the magnetic equator at the ground level and in low Earth orbit (LEO). Characterizing the variability of the EEJ provides insight into the role of vertical coupling across atmospheric layers in the Earth-space system.
Current numerical models do not capture the observed day-to-day variability of the EEJ, likely due to a lack of realistic representation of atmospheric tidal variability. However, it is challenging to fully describe these variabilities with limited observations. Ground-based magnetometers, while providing continuous measurements in time, lack in spatial coverage, and space-based measurements from LEO satellites provide global coverage but have significant gaps in temporal coverage for any given location. Here, we present a data-driven framework to model EEJ variability due to lower atmosphere tidal forcing by combining ground-based and space-based magnetic observations with physics-based models of the whole atmosphere and ionospheric electrodynamics.
In this study, magnetometer data from 51 ground-based stations spanning the region between +40° and -40° magnetic latitude are analyzed alongside data from the SWARM constellation from November 2019 to May 2020. The CHAOS-8.5 geomagnetic field model is used to isolate EEJ signals from the data. These observed EEJ signatures are then compared against simulated EEJ signals based on neutral wind dynamics simulated by the WACCM-X and a 3D model of the ionospheric dynamo system. The discrepancy between observed and simulated magnetic signatures of the EEJ will be used to impose observational constraints on the WACCM-X, enabling a more realistic representation of atmospheric tidal variability. The proposed framework overcomes the measurement gaps of current ionosphere-thermosphere observing systems and furthers our insight into the coupled dynamics of the lower and upper atmosphere.