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. Geospace is driven by disturbances at its outer boundary and interacts with the lower atmosphere at low altitudes. The different domains of geospace are populated by neutral gases and plasmas that are immersed in electromagnetic fields and evolve on disparate temporal and spatial scales. During storms, all of these domains become active and engage in complex, cross-scale interactions that profoundly alter the entire system. The complexity of the underlying physical processes defines the daunting challenge of predicting the most severe impacts of space weather on our technological infrastructure.

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

Untangling the web of causal connections in stormtime geospace is the challenge undertaken by the CGS.

CGS Science Objectives

Build a physics-based, predictive, community model of stormtime geospace which couples all key regions while treating and resolving critical mesoscale processes.

The space science community has made significant progress in developing theoretical and numerical models of geospace. Even so, the complexity of the coupled system has defied attempts to describe stormtime geospace with the completeness and fidelity required for comprehensive understanding and reliable space weather forecasting and mitigation.

There are three key requirements that a successful model of stormtime geospace must satisfy:

Because of the collective cross-scale interactions that define stormtime geospace, such understanding can only be derived from extensive development and analyses of simulation models that treat geospace as a whole.

There is strong evidence of the critical role of mesoscale processes in driving stormtime geospace dynamics across regions and spatiotemporal scales. Therefore, a key requirement for a "whole geospace model" is to 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, as well as subgrid modules to represent unresolved physical processes.

The forcing and preconditioning of the ionosphere and upper atmosphere from the lower atmosphere are increasingly recognized as an important intrinsic element of geospace activity. Thus, a "whole geospace model" must include full two-way coupling to the lower atmosphere.

To fulfill these requirements, in CGS we are building the Multiscale Atmosphere-Geospace Environment (MAGE) model.

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

It is only when models are constrained rigorously by observations and comprehensive data analysis that the degree of knowledge ultimately leading to robust predictive capability and application to operational needs can be achieved.

The advent of distributed global datasets presents new opportunities, which come with challenges as they provide measurements of multiple variables, but with different spatial and temporal resolutions.

Devising quantitative conditions that are necessary and sufficient for confirming model fidelity using such datasets can only be done in synergy between model developers and data analysts and providers.

Modern data mining techniques enable advanced validation methodologies and the ingestion or assimilation of datasets into model domains that historically have been challenging for data-model fusion, e.g., the magnetosphere.

The MAGE model is and 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.

The fully implemented MAGE model will make a transformative impact on the state of current knowledge about stormtime geospace. Armed with this model and its precise numerical techniques, CGS pursues pressing, previously unsolved science questions that have languished due to the lack of simulation tools and synergistic data-model analyses – the capabilities that CGS enables, implements and disseminates. A central theme of the CGS science investigation is the importance of mesoscale processes in enabling the full range of couplings, forcings and cross-scale interactions in stormtime geospace.

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 and facilitate a vibrant CGS-enabled research program beyond CGS.

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 the priorities of the Space Weather Strategy and Action Plan, the Decadal Survey for Solar and Space Physics, and the PROSWIFT act.

Engaging Broader Public

Bring our science to diverse audiences to expand public awareness of space weather

Our Team

CGS is proud of its uniquely qualified interdisciplinary team with unprecedented combination of theory, modeling, data analysis & computer science expertise.

The interdependence of all specialties encompassed by CGS and the CGS team structure, which integrates academic and research institutions, enable deep synergies within the team as well as with the larger Heliophysics community. CGS is led by the largest university affiliated research center in the Nation (JHU/APL) in partnership with the Nation’s premier center for atmospheric science (NCAR), four R1 universities (UNH, VT, Rice, UCLA), and a commercial entity (Syntek). To foster our broadening impacts, the CGS team include collaborators from Howard University, American Museum of Natural History, Maryland Science Center, and NASA Community Coordinated Modeling Center (CCMC). This diverse environment is poised for science breakthroughs; it is ideally suited for NASA workforce development as it enables deep knowledge integration across discipline and generational boundaries and provides robust career opportunities for students, postdoctoral and early career scientists.

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