Assimilative Mapping of Geospace Observations (AMGeO): Data Science
Tools for Collaborative Geospace Systems Science
Abstract
The most dynamic electromagnetic energy and momentum exchange processes
between the upper atmosphere and the magnetosphere take place in the
polar ionosphere, as evidenced by the aurora. Accurate specification of
the constantly changing conditions of high-latitude ionospheric
electrodynamics has been of paramount interest to the geospace science
community. In response this community’s need for research tools to
combine heterogeneous observational data from distributed arrays of
small ground-based instrumentation operated by individual investigators
with global geospace data sets, an open-source Python software and
associated web-applications for Assimilative Mapping of Geospace
Observations (AMGeO) are being developed and deployed
(https://amgeo.colorado.edu). AMGeO provides a coherent, simultaneous
and inter-hemispheric picture of global ionospheric electrodynamics by
optimally combining diverse geospace observational data in a manner
consistent with first-principles and with rigorous consideration of the
uncertainty associated with each observation. In order to engage the
geospace community in the collaborative geospace system science
campaigns and a science-driven process of data product validation, AMGeO
software is designed to be transparent, expandable, and interoperable
with established geospace community data resources and standards. This
paper presents an overview of the AMGeO software development and
deployment plans as part of a new NSF EarthCube project that has started
in September 2019.