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2018
Authors: Ma Q, Li W, Bortnik J, Thorne R M, Chu X., et al.
Title: Quantitative Evaluation of Radial Diffusion and Local Acceleration Processes During GEM Challenge Events
Abstract: We simulate the radiation belt electron flux enhancements during selected Geospace Environment Modeling (GEM) challenge events to quantitatively compare the major processes involved in relativistic electron acceleration under different conditions. Van Allen Probes observed significant electron flux enhancement during both the storm time of 17–18 March 2013 and non–storm time of 19–20 September 2013, but the distributions of plasma waves and energetic electrons for the two events were dramatically different. During 17–18 March 2013, the SYM‐H minimum reached −130 nT, intense chorus waves (peak Bw ~140 pT) occurred at 3.5 < L < 5.5, and several hundred keV to several MeV electron fluxes increased by ~2 orders of magnitude mostly at 3.5 < L < 5.5. During 19–20 September 2013, th. . .
Date: 03/2018 Publisher: Journal of Geophysical Research: Space Physics DOI: 10.1002/2017JA025114 Available at: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JA025114
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2017
Authors: Chu X., Bortnik J, Li W, Ma Q, Denton R., et al.
Title: A neural network model of three-dimensional dynamic electron density in the inner magnetosphere
Abstract: A plasma density model of the inner magnetosphere is important for a variety of applications including the study of wave-particle interactions, and wave excitation and propagation. Previous empirical models have been developed under many limiting assumptions and do not resolve short-term variations, which are especially important during storms. We present a three-dimensional dynamic electron density (DEN3D) model developed using a feedforward neural network with electron densities obtained from four satellite missions. The DEN3D model takes spacecraft location and time series of solar and geomagnetic indices (F10.7, SYM-H, and AL) as inputs. It can reproduce the observed density with a correlation coefficient of 0.95 and predict test data set with error less than a factor of 2. Its predict. . .
Date: 09/2017 Publisher: Journal of Geophysical Research: Space Physics DOI: 10.1002/2017JA024464 Available at: http://onlinelibrary.wiley.com/doi/10.1002/2017JA024464/full
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