CGS at a Glance

One of the critical grand challenges of Solar and Space Physics today is understanding and predicting stormtime geospace spanning altitudes from a few tens to millions of kilometers.

The term "geomagnetic storms", coined by Alexander von Humboldt, originates from 19th century observations of sunspots (most notably by Richard Carrington) coinciding with strong perturbations of the geomagnetic field and displays of aurorae. Geomagnetic storms are a consequence of complex plasma disturbances that start at the surface of the sun and then propagate through, and interact with, the interplanetary space environment, before impacting Earth. Storms occur in near-Earth space in response to this increased energy input from the solar wind, especially when coupled with certain interplanetary magnetic field orientations. Storms can have different solar and interplanetary drivers but intense and extreme events often involve series of coronal mass ejections (CMEs) or composites of structures including CMEs and corotating interaction regions.

Stormtime geospace is a system of systems representing interconnected physical domains of the near-Earth environment: the magnetosphere, including all of its regions; the ionosphere; the upper atmosphere in which the ionosphere is embedded; and the lower atmosphere. These domains are populated by neutral gases and plasmas that are immersed in electromagnetic fields and evolve on different temporal and spatial scales. During geospace storms, all of these domains become active and engage in complex, cross-scale interactions that profoundly alter the entire system.

To highlight their pervasive effects throughout geospace, we refer to geomagnetic storms as geospace storms.

CGS Objectives

The space science community has made significant progress in developing theoretical and numerical models of geospace.

Even so, the complexity of the coupled geospace system has defied attempts to describe stormtime geospace with the completeness and fidelity required for comprehensive understanding and reliable space weather forecasting and mitigation.

The scientific challenges are both physical and computational. On one hand, because of the collective cross-scale interactions that define stormtime geospace, such understanding can only be derived by treating geospace as a whole. On the other hand, there is strong evidence of the critical role of smaller, mesoscale processes in driving and mediating stormtime geospace dynamics across regions and spatiotemporal scales.

A whole geospace model must resolve the coupled system's dynamics across a broad range of scales.

This entails the conception, implementation, and use of highly accurate numerical algorithms running on powerful supercomputers.

INNOVATE community modeling capabilities by building a physics-based, predictive model of stormtime geospace, coupling all key regions and resolving critical mesoscale processes.

We call the whole geospace model we will build the Multiscale Atmosphere-Geospace Environment (MAGE) model. MAGE will span the domains of geospace, from the lower atmosphere to the ionosphere, to the different regions of the magnetosphere. MAGE will resolve global dynamics and critical mesoscale processes throughout geospace with highly precise numerical techniques.

EMPOWER the MAGE model by ingesting heterogeneous data sources and developing rigorous validation methodologies using multivariate datasets.

The advent of distributed global datasets presents new opportunities for data-model fusion and for rigorous model validation. Space and ground-based observations provide many types of measurements with different spatial and temporal resolutions, requiring synergy between model developers and data analysts and providers to devise proper conditions for assessing model fidelity. Modern data mining techniques enable advanced validation methodologies and the ingestion or assimilation of these datasets into model domains that historically have been challenging for data-model fusion, such as the magnetosphere. The MAGE model will be robustly tested with these datasets and techniques to verify model capabilities across the geospace domains and critical scales.

DISCOVER, understand, and quantify the causal connections and emergent dynamics across spatiotemporal scales, domains, species, and energy populations characteristic of stormtime geospace.

Armed with the data-constrained MAGE model, the CGS team will pursue pressing scientific issues that have remained unsolved until now, due to the lack of simulation tools and synergistic data-model analyses. A central theme of the CGS science investigation, enabled by the MAGE model, is the importance of mesoscale processes and cross-scale coupling in emergent global-scale dynamics.

CGS Benefits to Science & Beyond

Addressing a Grand Science Challenge

Stormtime geospace is a "system of systems" that manifests some of the most complex and least understood multiscale interactions in heliophysics.

Empowering the Heliophysics Community

Deliver an open-source whole geospace, multi-physics simulation model for community use.

Developing the New Heliophysics Workforce

Ensure training of a new, diverse generation of scientists with deep knowledge integration across physical domains, space science disciplines, and approaches, including theory, modeling, data analysis and computer science.

Supporting NASA and NSF Programs

Enable synergies with existing and future NASA missions, ongoing NASA grant programs, and NSF facilities.

Advancing Space Weather Preparedness

Build capabilities to nowcast and predict regional and global space weather, fulfilling priorities of the Space Weather Action Plan and Decadal Survey.


CGS is proud of its uniquely qualified interdisciplinary team with unprecedented combination of theory, modeling, data analysis & computer science expertise. The Center is led by JHU/APL, the largest university affiliated research center in the Nation, in partnership with a national research center (NCAR/HAO), three major universities with space science programs – University of New Hampshire (UNH), Virginia Tech (VT), and Rice University – and a nonprofit research center (SRI). To foster our broadening impacts, the CGS team include collaborators from Howard University, University of Maryland Eastern Shore, American Museum of Natural History, Maryland Science Center, and NASA Community Coordinated Modeling Center (CCMC). View our team >