Application and testing of the L neural network with the self-consistent magnetic field model of RAM-SCB

Authors

Yiqun Yu, Los Alamos National Laboratory
Josef Koller, Los Alamos National Laboratory
Vania K. Jordanova, Los Alamos National LaboratoryFollow
Sorin G. Zaharia, Los Alamos National Laboratory
R. Friedel, Los Alamos National Laboratory
S. K. Morley, Los Alamos National LaboratoryFollow
Yue Chen, Los Alamos National Laboratory
D. N. Baker, University of Colorado BoulderFollow
Geoffrey Reeves, Los Alamos National LaboratoryFollow
Harlan E. Spence, University of New HampshireFollow

Abstract

Abstract

We expanded our previous work on L neural networks that used empirical magnetic field models as the underlying models by applying and extending our technique to drift shells calculated from a physics-based magnetic field model. While empirical magnetic field models represent an average, statistical magnetospheric state, the RAM-SCB model, a first-principles magnetically self-consistent code, computes magnetic fields based on fundamental equations of plasma physics. Unlike the previous L neural networks that include McIlwain L and mirror point magnetic field as part of the inputs, the new L neural network only requires solar wind conditions and the Dst index, allowing for an easier preparation of input parameters. This new neural network is compared against those previously trained networks and validated by the tracing method in the International Radiation Belt Environment Modeling (IRBEM) library. The accuracy of all L neural networks with different underlying magnetic field models is evaluated by applying the electron phase space density (PSD)-matching technique derived from the Liouville's theorem to the Van Allen Probes observations. Results indicate that the uncertainty in the predicted L is statistically (75%) below 0.7 with a median value mostly below 0.2 and the median absolute deviation around 0.15, regardless of the underlying magnetic field model. We found that such an uncertainty in the calculated L value can shift the peak location of electron phase space density (PSD) profile by 0.2 R_E radially but with its shape nearly preserved. Key Points L* neural network based on RAM-SCB model is developed L* calculation accuracy is estimated by PSD matching using RBSP data L* uncertainty causes a radial shift in the electron phase space density profile.

Department

Physics

Publication Date

3-2014

Journal Title

Journal of Geophysical Research: Space Physics

Publisher

American Geophysical Union Publications

Digital Object Identifier (DOI)

10.1002/2013JA019350

Document Type

Article

Recommended Citation

Yu, Y., J. Koller, V. K. Jordanova, S. G. Zaharia, R. W. Friedel, S. K. Morley, Y. Chen, D. Baker, G. D. Reeves, and H. E. Spence (2014), Application and testing of the L∗ neural network with the self-consistent magnetic field model of RAM-SCB, J. Geophys. Res. Space Physics, 119, 1683–1692, doi:10.1002/2013JA019350.

Rights

©2014. American Geophysical Union. All Rights Reserved.