loading page

Novel approaches to geospace particle transfer in the digital age: Progress through data science
  • +9
  • Ryan McGranaghan,
  • Enrico Camporeale,
  • Kristina Lynch,
  • Jesper Gjerloev,
  • Téo Bloch,
  • Spencer Hatch,
  • Binzheng Zhang,
  • Pete Riley,
  • Mathew Owens,
  • Yuri Shprits,
  • Irina Zhelavskaya,
  • Susan Skone
Ryan McGranaghan

Corresponding Author:ryan.mcgranaghan@gmail.com

Author Profile
Enrico Camporeale
Author Profile
Kristina Lynch
Dartmouth College
Author Profile
Jesper Gjerloev
Author Profile
Téo Bloch
University of Reading
Author Profile
Spencer Hatch
University of Bergen
Author Profile
Binzheng Zhang
Hong Kong University
Author Profile
Pete Riley
Predictive Science Inc.
Author Profile
Mathew Owens
University of Reading
Author Profile
Yuri Shprits
University of California Los Angeles
Author Profile
Irina Zhelavskaya
GFZ Potsdam
Author Profile
Susan Skone
University of Calgary
Author Profile


The magnetosphere, ionosphere and thermosphere (MIT) act as a coherently integrated system (geospace), driven in part by solar influences and characterized by variability and complexity. Among the most important and yet uncertain aspects of the geospace system is energy and momentum coupling between regions, which is, in part, accomplished by the transfer of charged particles from the magnetosphere to the ionosphere in a process known as particle precipitation, and in the opposite direction by ion outflow. Both processes are inherently multiscale and manifest the variabilities and complexities of the geospace system. Despite the importance of the transfer of particles, existing models are increasingly ill-equipped to provide the specification necessary for the growing demand for geospace now- and forecasts. Due to recent trends in the availability of data, we now face an exciting opportunity to progress particle transfer in geospace through the intersection of traditional approaches and state-of-the-art data-driven sciences. We reveal novel particle transfer models utilizing machine learning (ML), present results from the models, and provide an evaluation of their capabilities including comparisons with observations and the current ’state-of-the-art’ models (e.g., OVATION Prime for particle precipitation and the Gamera-Ionosphere Polar Wind Model for ion outflow). We detail the data wrangling required to utilize the available geospace observations to make progress on the long-standing challenge of particle transfer and place specific emphasis on the discovery possible when ML models are appropriate and robustly interrogated in the context of physical understanding. Our presentation helps illustrate the trends in the application of data science in space science.