Handling the complexity of the space weather system: Novel approaches through particle precipitation and ion outflow

Abstract:

The magnetosphere, ionosphere and thermosphere (MIT) comprise the integrated system known as geospace. Geospace is variable and highly complex, and current models of processes as basic as mechanical momentum and energy transfer (i.e., particle precipitation and ion outflow) are subject to high uncertainty. The challenge this problem poses is reflected in our community’s realization that “the inherent complexity of space weather requires developing new approaches to predictive modeling.” We share the results of research on machine learning prediction of particle precipitation and ion outflow, and quantification of the related uncertainties. These results represent the efforts of an International Space Sciences Institute team and multiple additional projects.They give new understanding of the information content of solar wind data as it relates to prediction of geospace processes; encompass a new approach for representing solar wind and geospace data together; and yield a roadmap for the future of geospace prediction.

We will discuss these findings and highlight structures for collaboration that have been developed and tested that speak to this session’s goals. We show how the community can adopt these results, both now and in the future, to synergize our collaborative research and enable real-time prediction of conditions in geospace.