Collaborative Research: AI-enabled, Uncertainty-Aware Space Weather Risk Characterization and Mitigation for Satellite Operations
- Institution
- George Mason University, VA
- Principal investigator
- Edward J Oughton
- Funding
- $500K
- Duration
- 2026–2030
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Still being scored
Abstract
Modern society depends on satellites for communications, navigation, weather forecasting, disaster response, financial systems, and national security. The ever increasing number of satellites face hazards from severe space weather, which can disrupt their operations and increase the risk of costly failures. This project will develop new tools that help satellite operators anticipate and manage space weather risks, strengthening the resilience of the nation's space infrastructure and the services it enables. The project will also educate and train the next generation of scientists and engineers, develop new educational materials related to space weather, engage K-12 students in science and engineering, and release open-source software to accelerate innovation across the space community. This project will develop the first end-to-end framework for translating uncertainty in space weather forecasts into actionable risk information for satellite operations. The research combines probabilistic forecasting of geomagnetic storms, physics-based modeling of the Earth's upper atmosphere, uncertainty propagation of satellite trajectories through orbital dynamics, and quantitative assessment of operational and economic impacts. Machine learning and physics-based models will be integrated to characterize geomagnetic storm occurrence, timing, and severity, quantify their effects on atmospheric drag, and estimate the resulting uncertainty in satellite trajectories and operational decisions. The framework will produce probabilistic measures of conjunction risk, maneuver burden, orbital degradation, and service disruption, together with estimates of their broader economic and societal impacts. Validation will be performed using historical geomagnetic storms and commercial satellite tracking data. The resulting open-source framework will provide a foundation for uncertainty-aware satellite operations and future space weather decision-support capabilities. 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.
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