Abstract
AbstractWe 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.
Highlights
Electron precipitation is a key component linking the ionosphere and the magnetosphere
We present a new nowcast model of total electron energy flux based on LEO measurements of electron precipitation, namely the Defense Meteorological Satellite Program (DMSP) satellites https://cdaweb.gsfc. nasa.gov/pub/data/dmsp/ (Redmon et al, 2017), driven by a combination of solar wind parameters and state descriptors and their time histories
Recognizing the need to more accurately represent the information in these data, we explore more capable relationships between the input solar wind and state descriptors and output particle precipitation via machine learning (ML)
Summary
Electron precipitation is a key component linking the ionosphere and the magnetosphere. Electrons in the magnetosphere-ionosphere (MI) system carry current, transport energy, and precipitate (i.e., follow magnetic field lines from the magnetosphere to the ionosphere) to collide with the neutral atmosphere thereby driving changes in the electrical conductivity tensor This tensor is central to the three-dimensional electrical current circuit that flows over vast distances between the magnetosphere and the ionosphere. Abrupt and intense precipitation events are associated with likewise abrupt and large amplitude changes in the ionospheric electron density and currents that drive ground-induced currents Such variability is linked to a number of potentially hazardous impacts, including errors in directional drilling, power grid transformer disruption, loss of Global Navigation Satellite Systems (GNSS) communication, and corresponding timing and position accuracy (Cannon et al, 2013). These are effects that need to be mitigated for the functioning of our increasingly technologically dependent society
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