Primary Convener:
Aaron Bukowski, University of Michigan

Convener:
Jeffrey Thayer, University of Colorado Boulder
Thomas Berger, University of Colorado
Pauline Dredger, University of Michigan Ann Arbor

Chair:
Aaron Bukowski, University of Michigan
Pauline Dredger, University of Texas at Arlington

Geospace is defined as the region of space delineated by the interaction of the magnetosphere and upper atmosphere with the solar wind, encompassing both orbital and cis-lunar space. The rapidly increasing number of operational satellites and space debris in geospace, particularly in Low Earth Orbit, requires improved forecasting models to ensure the safety and sustainability of human exploration and satellite operations. This session focuses on improving nowcasts and forecasts of the geospace environment through developments in theory, numerical simulation and empirical models, observations, data assimilation, machine learning and other techniques that merge models and data. We invite presentations on physics-based and data-driven modeling of the geospace system, theoretical aspects of this system and its interaction with solar outputs; existing, new, or planned space weather observations, both space- and ground-based; as well as methods to improve nowcasting and forecasting of geospace variability through data assimilation and development of machine learning models.

Index Terms
2447 Modeling and forecasting
2722 Forecasting
7924 Forecasting
7974 Solar effects

Neighborhoods:
4. Beyond Earth

Cross-Listed:
SM - SPA-Magnetospheric Physics