Predicting the electron population of Earth’s ring current during geomagnetic storms still remains a challenging task. In this work, we investigate the sensitivity of 10 keV ring current electrons to different driving processes, parameterised by the Kp index, during several moderate and intense storms. Results are validated against measurements from the Van Allen Probes satellites. Perturbing the Kp index allows us to identify the most dominant processes for moderate and intense storms respectively. We find that during moderate storms (Kp < 6) the drift velocities mostly control the behaviour of low energy electrons, while loss from wave-particle interactions is the most critical parameter for quantifying the evolution of intense storms (Kp > 6). Perturbations of the Kp index used to drive the boundary conditions at GEO and set the plasmapause location only show a minimal effect on simulation results over a limited L range. It is further shown that the flux at L ∼ 3 is more sensitive to changes in the Kp index compared to higher L shells, making it a good proxy for validating the source-loss balance of a ring current model.
The Earth’s magnetic field traps charged particles which are transported longitudinally around Earth, generating a near-circular current, known as the ring current. While the ring current has been measured on the ground and space for many decades, the enhancement of the ring current during geomagnetic storms is still not well understood, due to many processes contributing to its dynamics on different time scales. Here, we show that existing ring current models systematically overestimate electron flux observations of 10–50 keV on the nightside during storm onset. By analyzing electron drift trajectories, we show that this systematic overestimation of flux can be explained through a missing loss process which operates in the pre-midnight sector. Quantifying this loss reveals that the theoretical upper limit of loss has to be reached over a broad region of space in order to reproduce the observations. This missing loss may be attributed to inaccuracies in the parameterization of the loss due to chorus wave interactions, combined with the scattering by electrostatic electron cyclotron harmonic waves which is currently not included in ring current models.
The plasmasphere is a region of cold and dense plasma around the Earth, corotating with the Earth. Its plasma density is very dynamic under the influence of the solar wind and it influences several processes such as the GPS navigation, the surface charging of the satellites and the propagation and growth of plasma waves. In this manuscript, we present a new machine-learning model of the equatorial plasma density depending only on the Kp index and the solar-wind properties at the L1 Lagrange point. We call this model PINE-RT as it has been inspired by the recently-introduced PINE (Plasma density in the Inner magnetosphere Neural network-based Empirical) model and it has been developed to run in real-time (RT) in the context of the PAGER project. This project is an EU Horizon 2020 project aiming at forecasting the threats of satellite charging as a consequence of the solar activity 1–2 days ahead. In PAGER, the Kp index and the solar-wind properties at L1 are the inputs which are made available for the plasmasphere modeling. We report here the detailed derivation of the PINE-RT model and its validation and comparison with two state-of-the-art machine-learning and physics-based models. The model is currently running in real-time and its predictions are publicly available.
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