2025 Workshop: Geospace Model-Data Comparisons

During geomagnetic storms, the magnetosphere-ionosphere-thermosphere (MIT) system becomes energized and engages in complex, multi-scale interactions between the physical domains. Mesoscale processes, both singularly and collectively, can profoundly impact the global evolution of the geospace system. This is particularly true in the magnetosphere’s nightside transition region as well as the low and mid-latitude ionosphere-thermosphere region that it maps to. Recent advances in the resolving power and coupling complexity of global geospace modeling have enabled investigations into the drivers and impacts of these phenomena. In order to understand the system-level behavior of storm-time geospace it is necessary to combine observational data in various forms with global models in ways that both validate the models and use them to contextualize the observations.
The NASA DRIVE Center for Geospace Storms (CGS) aims to develop a physics-based, predictive community model of storm-time geospace that couples all key regions while resolving critical mesoscale processes. The extensive network of in-situ and ground-based instrumentation across the MIT system provides a valuable foundation for improving data-model comparisons. However, leveraging these observational resources effectively requires a strategic approach and sustained collaboration between the modeling and data communities, as well as the development of methods to validate the system-level behavior of geospace models.
This workshop will focus on the key question: How can we best utilize existing observational data to develop robust, quantitative methods for model validation and comparison to assess how well the models reproduce the characteristic behavior of the system? As part of an ongoing collaboration between CGS and the GEM MESO focus group, this session will engage (and connect?) both the GEM and CEDAR communities in this endeavor. We aim to bring together the modeling and data communities to foster discussions on best practices, current challenges, and future opportunities for advancing geospace model/data comparisons, which is key to using simulations to understand the physical processes driving the system.