Recent advances in the collection and analysis of large-scale datasets have led to impressive progress in modeling individual regions of the near-Earth environment from ground to several Earth radii: thermosphere, ionosphere, plasmasphere, and magnetosphere. Machine learning approaches have shown remarkable results for these complex systems. However, these geospace "spheres" are highly interconnected, with nonlinear and time-dependent interactions that may not be fully captured by models focusing on individual regions.
This session addresses the central research question:
How can we accurately model the interconnections and feedback between the thermosphere, ionosphere, plasmasphere, and magnetosphere?
We especially welcome contributions on:
- Data-driven modeling and global-scale interactions between these regions
- Combination of multiple measurement techniques, such as low Earth orbit (LEO) satellites or remote sensing.
- Identification and incorporation of external drivers for accurate modeling of the response of the near-Earth environment to space weather.
- Innovative approaches that include data assimilation, machine learning, empirical or numerical modeling.
The session aims to foster multidisciplinary collaborations toward more integrated and comprehensive modeling of the near-Earth environment.
Yuri Shprits