Next-Generation Data Assimilation Methods for Polar Ionospheric Electrodynamics
Journal Article
Overview
abstract
Abstract; Accurately specifying polar ionospheric electrodynamics is essential for understanding energy and momentum exchange between space and the upper atmosphere and for improving simulations of the ionosphere and the thermosphere. Statistical models are commonly used to provide input for global circulation models (GCMs). However, maps derived from simultaneous multi-instrument observations better represent the actual state of the system. Such maps integrate measurements from ground-based magnetometers and radars, in situ plasma and magnetic field sensors at low-Earth orbit, and optical and particle observations of auroral precipitation. However, ionospheric data assimilation remains in its early stages. Current methods rely on restrictive assumptions to simplify equations and stabilize inverse problems, but these constraints limit applicability beyond polar regions, hinder the inclusion of time-dependent processes, and prevent independent estimation of ionospheric conductance. This review examines the physical foundations of ionospheric data assimilation, evaluates the limitations of existing approaches, and explores pathways toward more accurate and flexible techniques. Specifically, we discuss approaches to: (1) use a common dataset to estimate conductance and fields in a single inversion; (2) incorporate neutral winds instead of assuming they are zero; (3) account for a realistic main magnetic field geometry instead of assuming radial field lines; (4) eliminate a sharp boundary between polar and low-latitude regions; (5) use F-region density measurements to capture the history of ionospheric conductance and plasma transport; (6) account for the magnetic field of ground-induced currents in a more realistic way; (7) include ionospheric induction effects to stabilize time-dependent inversions; and (8) couple ionospheric electrodynamics with global magnetosphere simulations to model the physics of time variations.
