  We advance the modeling capability of electron particle precipitation from
the magnetosphere to the ionosphere through a new database and use of machine
learning (ML) tools to gain utility from those data. We have compiled, curated,
analyzed, and made available a new and more capable database of particle
precipitation data that includes 51 satellite years of Defense Meteorological
Satellite Program (DMSP) observations temporally aligned with solar wind and
geomagnetic activity data. The new total electron energy flux particle
precipitation nowcast model, a neural network called PrecipNet, takes advantage
of increased expressive power afforded by ML approaches to appropriately
utilize diverse information from the solar wind and geomagnetic activity and,
importantly, their time histories. With a more capable representation of the
organizing parameters and the target electron energy flux observations,
PrecipNet achieves a >50% reduction in errors from a current state-of-the-art
model oval variation, assessment, tracking, intensity, and online nowcasting
(OVATION Prime), better captures the dynamic changes of the auroral flux, and
provides evidence that it can capably reconstruct mesoscale phenomena. We
create and apply a new framework for space weather model evaluation that
culminates previous guidance from across the solar-terrestrial research
community. The research approach and results are representative of the "new
frontier" of space weather research at the intersection of traditional and data
science-driven discovery and provides a foundation for future efforts.
