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  • Walach_Grocott_2025_TiVIE

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Modeling the Time‐Variability of the Ionospheric Electric Potential (TiVIE)

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
Article numbere2024SW004139
<mark>Journal publication date</mark>31/07/2025
<mark>Journal</mark>Space Weather
Issue number7
Volume23
Publication StatusE-pub ahead of print
Early online date16/07/25
<mark>Original language</mark>English

Abstract

We present a statistical model of the ionospheric electric potential derived from line‐of‐sight plasma velocity measurements from the Super Dual Auroral Radar Network. Electric potential patterns are produced using an established technique that models the ionospheric electric potential as a spherical harmonic expansion. Improvements over existing models are achieved by the use of novel parameterizations that capture three major sources of time‐variability of the coupled solar wind‐magnetosphere‐ionosphere system. The first source of variability relates directly to the time‐dependence of the system on the upstream solar wind conditions, specifically the strength and orientation of the interplanetary magnetic field. The magnetosphere‐ionosphere system is not static under continuous driving by the solar wind but evolves with time, even if the solar wind conditions themselves remain steady. We account for this by defining a solar wind steadiness timescale with which we parameterize the electric potential. The second source of variability relates to the storage and release of energy in the magnetosphere that is associated with magnetospheric substorms. The electric potential evolves throughout the substorm cycle, and its morphology is strongly influenced by the location of substorm onset. We therefore parameterize by substorm onset location and the time relative to substorm onset. Lastly we account for the variability introduced by geomagnetic storms. The ionospheric electric potential evolves differently through each phase of a storm, so we parameterize by storm phase. We discuss the details of the model, and assess its performance by comparison to other models and to observations.