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

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.

Radiation belt data assimilation of a moderate storm event using a magnetic field configuration from the physics-based RAM-SCB model

“…The T01S model is designed specifically for magnetic storms, whereas the T96 model is more general. These models have been widely used in the radiation belt data assimilation studies (e.g., Shprits et al, 2007;Daae et al, 2011;Koller et al, 2007), but their imperfection can pose considerable biases in the adiabatic invariants especially during highly disturbed time (e.g., Huang et al, 2008;Yu et al, 2012bYu et al, , 2014 and is expected to influence the subsequent assimilated PSD state.…”

Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach

“…The contributions of each component current system are parametrized by upstream solar wind measurements and Improving models of the geomagnetic field is relevant to a broad range of studies. There are many common uses of these models including the calculation of the adiabatic invariants for radiation belt studies (e.g., Iles et al, 2006;Turner et al, 2014;Yu et al, 2014), calculation of conjunction between satellites(e.g., Friedel et al, 2005), calculation of mapping from the ionosphere to the magnetotail or vice versa (e.g., Antonova et al, 2015;Ge et al, 2012;Kubyshkina et al, 2011).…”

A new method to study the time correlation between Van Allen Belt electrons and earthquakes

“…The inner belt zone is defined as 1.25 L * 2.45, based on the OP77Q external field (Olson and Pfitzer 1977) and IGRF internal field models, where L * is the Roederer L shell associated with the third adiabatic invariant, i.e., the magnetic flux enclosed by the particles guiding drift shell (Roederer 1970;Koller, Reeves, and Friedel 2009;Yu, Koller, and Jordanova 2012;Yu et al 2014).…”