News and Progress | Novel approaches to multiscale geospace particle transfer: Improved understanding and prediction through uncertainty quantification and machine learning

Conferences:

American Geophysical Union 2020

Camporeale, E., McGranaghan, R., et al. “Machine Learning in Space Weather.” Conference Session. https://agu.confex.com/agu/fm20/meetingapp.cgi/Session/108141
McGranaghan, R., et al. “Handling the complexity of the space weather system: Novel approaches through particle precipitation and ion outflow.”
McGranaghan, R., et al., “Tracing Solar Activity Through the Heliosphere: Lessons from Comparative Solar Minima.” Conference Session. https://agu.confex.com/agu/fm20/meetingapp.cgi/Session/110212

American Geophysical Union 2019

Camporeale, E., McGranaghan, R., et al. “Machine Learning in Space Weather.” Conference Session. https://agu.confex.com/agu/fm19/meetingapp.cgi/Session/87144
McGranaghan, R., et al. “TH45B – Antidisciplinary: Science and Engineering in the Digital Age.” Town Hall. https://agu.confex.com/agu/fm19/meetingapp.cgi/Session/76838
McGranaghan, R., et al. “SM31D-3190 – Novel approaches to geospace particle transfer in the digital age: Progress through data science.” Poster. https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/513582
Taziny, A., and Camporeale, E. “NG31A-0851 – Nowcasting of auroral electron precipitation using an artificial neural network.” Poster. https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/512368

European Geophysical Union 2019: Hatch, S., et al. “Southern and Northern Hemisphere relationships between ion outflowand magnetic field fluctuations.” https://meetingorganizer.copernicus.org/EGU2019/EGU2019-6566.pdf

Coupling, Energetics, and Dynamics of Atmospheric Regions (CEDAR)

McGranaghan, R., et al. “Geospace Data Science.” Session. http://cedarweb.vsp.ucar.edu/wiki/index.php/2019_Workshop:Geospace_Data_Science
McGranaghan, R., et al. “Ion Upflow and Outflow.” Session (joint with the Geospace Environment Modeling (GEM) Workshop). https://docs.google.com/document/d/13tgjUAhRmpw7kK1W08p7ntRbYQhURdlAVB3JSH1MN-I/edit

Machine Learning in Heliophysics Conference: McGranaghan, R., et al. “What is the social engineering challenge of data science for Heliophysics and how do we solve it?” Featured Talk.

Publications:

R. M. McGranaghan, J. Ziegler, T. Bloch, S. Hatch, E. Camporeale, K. Lynch, M. Owens, J. Gjerloev, B. Zhang, and S. Skone, “Next generation particle precipitation: Mesoscale prediction through machine learning (a case study and framework for progress),” arXiv, https://arxiv.org/abs/2011.10117.

Eos.org feature article: McGranaghan and Camporeale [2019] “Eight Lessons I Learned Leading a Scientific ‘Design Sprint.’”

Teaser: Applying the fast-paced technique, pioneered by Google to spur rapid innovation, to space science yielded unexpected benefits and may be a model for collaborations across many scientific disciplines.

Hatch, S., Moretto Jorgensen, T., Lynch, K., Laundal, K., Gjerloev, J. and Lund, E. (2019). Relationship between cusp-region ion outflows and east-west magnetic field fluctuations in Southern and Northern Hemispheres. Earth and Space Science Open Archive.

Hatch, S. M., Moretto, T., Lynch, K. A., Laundal, K. M., Gjerloev, J. W., & Lund, E. J. (2020). The relationship between cusp‐region ion outflows and east‐west magnetic field fluctuations at 4000‐km altitude. Journal of Geophysical Research: Space Physics, 125, e2019JA027454. https://doi.org/10.1029/2019JA027454

Significant Activities:

Highlighted during Doug Rowland (NASA Goddard Space Flight Center) “Mechanisms of Energetic Mass Ejection – eXplorer (MEME-X)” presentation in April 2019.

Submitted response to the “NASA Frontier Development Laboratory (FDL) Request for Information (RFI)” to create a potential challenge concept for NASA’s FDL 2020 program.