Collaborative research: cedar – space weather impacts on earth's exospheric energetics through physics-based and machine learning methods

The National Science Foundation Office of Integrative Activities awarded Catholic University of America $180,454 under the Geosciences program (CFDA 47.050) on September 1, 2026, to develop physics-based and machine learning methods for modeling space weather impacts on Earth's exospheric energetics.

The recipient will combine exosphere modeling, machine learning techniques, and ultraviolet observations to determine processes responsible for storm-time changes in the hydrogen exosphere. The work uses an exosphere model to simulate the global density and energy distribution of neutral hydrogen under quiet and disturbed space weather conditions, driven by thermospheric boundary conditions and coupled with inner-magnetospheric plasma populations to quantify effects of thermal processes, solar radiation pressure, and charge exchange within plasmasphere and ring-current ions. Model results will be evaluated against Lyman-alpha observations of the geocorona—a faint glowing cloud of atomic hydrogen extending from approximately 500 kilometers altitude through the near-Earth plasma environment. The research addresses current knowledge gaps in atmospheric escape, interpretation of ultraviolet observations, and satellite drag and orbital lifetime constraints during geomagnetic storms.

The project runs through August 31, 2029, with performance in Washington, DC. The award supports workforce development in space weather research and STEM fields.

Abstract

Earth?s exosphere is the uppermost atmospheric region, extending from about 500 KM altitude to many earth radii into space. IT is dominated by atomic hydrogen, which absorbs and RE-emits solar ultraviolet light, producing a faint glowing cloud known as the geocorona. Although extremely tenuous, this hydrogen cloud extends through the near-earth plasma environment, allowing neutral atoms and charged particles to interact across a large region of space. During geomagnetic storms, changes in the denser atmosphere below the exosphere and in the surrounding charged-particle environment can modify the density and energy distribution of exospheric hydrogen. These changes remain poorly quantified, limiting our ability to understand atmospheric escape, interpret ultraviolet observations of the geocorona, and improve constraints on satellite drag and orbital lifetime estimates. The proposal intends to utilize upper atmosphere models and machine learning techniques to address these current knowledge gaps. The work will advance space weather research while preparing a technically skilled stem workforce capable of supporting innovation, resilience, and economic growth. The research team will combine physics-based modeling, machine learning, and ultraviolet observations to determine the processes responsible for storm-time changes in the hydrogen exosphere. The approach uses an exosphere model to simulate the global density and energy distribution of neutral hydrogen under quiet and disturbed space weather conditions. The model will be driven by thermospheric boundary conditions and coupled with inner-magnetospheric plasma populations to quantify the effects of thermal processes, solar radiation pressure, and charge exchange within plasmasphere and ring-current ions. Model results will be evaluated using lyman-alpha observations of the geocorona from space-based instruments, together with machine-learning methods that provide independent constraints on hydrogen density. The results of this work will benefit scientists who model earth?s upper atmosphere, planetary exospheres, and space weather impacts on near-earth space, while improving the use of ultraviolet remote sensing observations to study geospace. By providing better constraints on neutral hydrogen density, the project will also support improved representations of the upper-atmospheric neutral environment used in satellite drag and orbital lifetime estimates. 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.- subawards are not planned for this award.