GEM: Quantifying Local and Global Controls of Solar-wind/Magnetosphere Coupling

About This Grant

The solar wind—a stream of hot charged particles from the Sun—plays a crucial role in shaping Earth's magnetic environment, known as the magnetosphere. This interaction drives space weather, which can disrupt satellites, power grids, and communication systems. While scientists have long studied how the solar wind influences the magnetosphere, key gaps remain in understanding exactly which solar wind conditions matter most and how uncertainties in measurements may skew predictions. This project seeks to resolve the limitations on: (1) how the Earth's magnetic boundary alters the effect of the solar wind, and (2) how uncertainties and hidden correlations in solar wind data affect our understanding of its effects on the Earth's magnetic environment. By combining spacecraft observations, advanced simulations, and machine learning, this work will provide a clearer picture of solar wind-magnetosphere coupling—improving forecasts of geomagnetic storms. The project will benefit society by measurably improving our understanding of how solar wind drives space weather, which is critical for protecting satellites, aviation, and power systems. The team will develop educational resources on handling uncertainties in big data to support STEM workforce development in the United States. By addressing fundamental questions in space physics and developing tools for scientific education, this project aligns with NSF's mission to advance national prosperity and defense through scientific progress. The study will answer two key science questions: “How do local (magnetosheath) parameters influence geomagnetic response compared to global (solar wind) measurements?” and “Which specific solar wind and magnetosheath parameters best explain geomagnetic activity, and to what extent?”. By correlating multi-point satellite observations in the magnetosheath and Lagrange point-1 with geomagnetic indices, this work will quantify uncertainties and biases in solar wind data and improve the reliability of coupling functions used in space weather forecasting. An MHD model representing the coupled Magnetosphere-Ionosphere system will be used to compare simulated and observed magnetospheric responses, where the discrepancies will help quantify missing kinetic effects in global models. The results of the project will (1) advance space weather prediction by refining coupling functions; (2) resolve data limitations by systematically quantifying uncertainties in solar wind measurements; (3) guide improvements in global models by constraining missing physics; (4) support training of researchers in handling uncertainties and related statistical biases while using big data. The project will advance our understanding of solar wind-magnetosphere interactions while improving the reliability of space weather models, with broad implications for both scientific research and operational space weather forecasting. 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.