Author ORCID Identifier
Ryan McGranaghan: https://orcid.org/0000-0002-9605-0007
Téo Bloch: https://orcid.org/0000-0001-6017-1619
Spencer Hatch: https://orcid.org/0000-0001-7412-4936
Enrico Camporeale: https://orcid.org/0000-0002-7862-6383
Kristina Lynch: https://orcid.org/0000-0001-9006-1138
Mathew Owens: https://orcid.org/0000-0003-2061-2453
Binzheng Zhang: https://orcid.org/0000-0002-1555-6023
Document Type
Article
Publication Date
2021
Publication Title
Space Weather
Department
Department of Physics and Astronomy
Abstract
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.
DOI
10.1029/2020SW002684
Original Citation
McGranaghan, R. M., Ziegler, J., Bloch, T., Hatch, S., Camporeale, E., Lynch, K., et al. (2021). Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress). Space Weather, 19, e2020SW002684. https://doi.org/10.1029/2020SW002684
Dartmouth Digital Commons Citation
McGranaghan, Ryan C.; Ziegler, Jack; Bloch, Téo; Hatch, Spencer; Camporeale, Enrico; Lynch, Kristina A.; Owens, Mathew; Gjerloev, Jesper; Zhang, Binzheng; and Skone, Susan, "Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress)" (2021). Dartmouth Scholarship. 4297.
https://digitalcommons.dartmouth.edu/facoa/4297
