GEM: Resolving the Role of Low-Entropy Plasma Bubbles in Generating Prompt Penetration Electric Fields

Predicting the response of the equatorial ionosphere during geomagnetic storms remains one of the most persistent challenges in space weather science. The primary driver of these disturbances is the Prompt Penetration Electric Field (PPEF), which can cause rapid, large-scale redistributions of ionospheric plasma. This research identifies an unaccounted-for internal driver: “plasma bubbles.” These are low-entropy flux tubes originating from reconnection in the magnetotail. These bubbles act as independent modulators, transporting energy earthward at several hundred kilometers per second via the interchange instability. By injecting low-entropy plasma into the inner magnetosphere, these bubbles compete with external solar wind drivers, fundamentally altering the geospace environment’s electrodynamic state. These changes can disrupt satellite operations, high-frequency communications, navigation systems, and even terrestrial power grids. This project combines observational validation with theoretical modeling to uncover how these internal magnetic bursts reorganize the upper atmosphere. The project will help scientists build more realistic predictive models, ultimately leading to more reliable space-weather assessments for technologies that modern society relies on every day. Better space weather forecasting is important for protecting critical infrastructure, communication systems, navigation, and power grids. This study fills a critical gap in understanding how low-entropy plasma bubbles impact PPEF dynamics. The interaction between the magnetosphere and ionosphere is a non-linear coupling process involving the exchange of field-aligned currents, mass, and momentum. To accurately simulate this system, one must account for the high-speed dynamics of low-entropy plasma bubbles. This study combines advanced numerical modeling with multi-instrument observational validation. The computational approach integrates the Inertialized Rice Convection Model. Simulation outputs will be validated against ground-based and satellite observations. The team will use clear metrics, such as root-mean-square error comparisons of electric field timing, drift amplitudes, and recurrence statistics, to benchmark the models. Improved models will enhance space weather forecasting, benefiting satellite operations, navigation, and power grids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.