Energetic particles in the near-Earth space that are commonly referred to as Van Allen radiation belts, or trapped radiation, pose a significant risk to Earth-orbiting satellites and humans in space. During geomagnetic storms, the radiation in the near-Earth space can dramatically increase, and numerous anomalies are often reported by satellite operators. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the radiation belts. Up until recently, most of the research concentrated on the analysis of data from individual spacecraft, which does not allow for inferring the global evolution of the radiation environment. The space environment is a central focus of a number of ongoing international missions, including ESA’s Cluster mission, US NASA’s Van Allen Probes and THEMIS missions, US NOAA’s GOES satellites, the Japanese ERG mission, and a number of other international missions.
Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude.Standard data assimilation methods cannot be directly used to globally assimilate data in the radiation belts through utilizing all available data, as radiation varies by orders of magnitude in time and space. In this project, we propose to develop new methods that will enable efficient data assimilation from multiple satellite missions into complex physics-based models for the evolution of energetic and relativistic particles. We propose to combine state-of-the-art partial differential equation-based models of the inner magnetosphere Versatile Electron Radiation Belt (VERB-3D) with newly developed data assimilation methods to reconstruct the dynamics of the inner magnetospheric radiation, utilizing observations from various orbits. The developed data assimilation methods for systems where quantities may vary by several orders of magnitude may be used in the future for other applications in space sciences and potentially other areas of science and engineering.
The doctoral and postdoctoral researchers will work jointly on devising novel and implementing novel data assimilation schemes.
N. A. Aseev, Y. Y. Shprits, A. Y. Drozdov, A. C. Kellerman, M. E. Usanova, D. Wang, I. S. Zhelavskaya (2017). Signatures of Ultrarelativistic Electron Loss in the Heart of the Outer Radiation Belt Measured by Van Allen Probes. Journal of Geophysical Research, 122, 10102-10111. doi: 10.1002/2017JA024485
B. Ni, X. Cao, Y. Y. Shprits, D. Summers, X. Gu, S. Fu, Y. Lou (2018). Hot Plasma Effects on the Cyclotron-Resonant Pitch-Angle Scattering Rates of Radiation Belt Electrons Due to EMIC Waves. Geophysical Research Letters, 45, 21-30. doi: 10.1002/2017GL07602
I. S. Zhelavskaya, Y. Y. Shprits and M. Spasojevic (2017). Empirical modeling of the plasmasphere dynamics using neural networks. Journal of Geophysical Research: Space Physics, 122, 11227–11244. doi:10.1002/2017JA024406
J. E. Borovsky and Y. Y. Shprits (2017). Is the Dst Index Sufficient to Define All Geospace Storms?. Journal of Geophysical Research: Space Physics, 122, 11543-11547. doi:10.1002/2017JA024679