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 prog