• Distribution of energetic oxygen and hydrogen in the near-Earth plasma sheet 

      Kronberg, Elena A.; Grigorenko, Elena; Haaland, Stein; Daly, Patrick W.; Delcourt, Dominique C.; Luo, Hao; Kistler, Lynn M.; Dandouras, Iannis (Peer reviewed; Journal article, 2015-05)
      The spatial distributions of different ion species are useful indicators for plasma sheet dynamics. In this statistical study based on 7 years of Cluster observations, we establish the spatial distributions of oxygen ions ...
    • Exploring solar-terrestrial interactions via multiple imaging observers 

      Branduardi-Raymont, Graziella; Berthomier, M; Bogdanova, Y. V.; Carter, Jennifer; Collier, M; Dimmock, A; Dunlop, Malcolm; Fear, Robert C.; Forsyth, Colin; Hubert, Benoit; Kronberg, Elena A.; Laundal, Karl Magnus; Lester, Mark; Oksavik, Kjellmar; Østgaard, Nikolai; Palmroth, Minna; Plaschke, Ferdinand; Porter, F.S.; Rae, I. Jonathan; Read, Andy; Samsonov, A. A.; Sembay, Steven; Shprits, Yuri; Sibeck, David G.; Walsh, Brian; Yamauchi, M (Journal article; Peer reviewed, 2021)
      How does solar wind energy flow through the Earth’s magnetosphere, how is it converted and distributed? is the question we want to address. We need to understand how geomagnetic storms and substorms start and grow, not ...
    • Prediction of Soft Proton Intensities in the Near-Earth Space Using Machine Learning 

      Kronberg, Elena A.; Hannan, Tanveer; Huthmacher, Jens; Münzer, Marcus; Peste, Florian; Zhou, Ziyang; Berrendorf, Max; Faerman, Evgeniy; Gastaldello, Fabio; Ghizzardi, Simona; Escoubet, Philippe; Haaland, Stein; Smirnov, Artem; Sivadas, Nithin; Allen, Robert C.; Tiengo, Andrea; Ilie, Raluca (Journal article; Peer reviewed, 2021)
      The spatial distribution of energetic protons contributes to the understanding of magnetospheric dynamics. Based upon 17 yr of the Cluster/RAPID observations, we have derived machine-learning-based models to predict the ...