We present a proof of concept for the probabilistic emulation of the Ring current-Atmosphere interactions Model with Self-Consistent magnetic field (RAM-SCB) particle flux. We extend the workflow developed by Licata and Mehta (2023) by applying it to the ring current and further developing its uncertainty quantification methodology. We introduce a novel approach for sampling over 20 years of solar and geomagnetic activity to identify 30 simulation periods, each one week long, to generate the training, validation, and test datasets. Large-scale physics-based simulation models for the ring current can be computationally expensive. This work aims at creating an emulator that is more efficient, capable of forecasting, and provides an estimate on the uncertainty of its predictions, all without requiring large computational resources. We demonstrate the emulation process on a subset of particle flux: a single energy channel of omnidirectional flux. A principal component analysis (PCA) is used for the dimensionality reduction into the reduced-space, and the dynamic modeling is performed with a recurrent neural network. A hierarchical ensemble of Long-Short Term Memory (LSTM) neural networks provides the statistics needed to produce a probabilistic output, resulting in a reduced-order probabilistic emulator (ROPE) that performs time-series forecasting of the ring current’s particle flux with an estimate on its uncertainty distribution. The resulting ROPE from this smaller subset of RAM-SCB particle flux provides dynamic predictions with errors less than 11% and calibration scores under 10%, demonstrating that this workflow can provide a probabilistic emulator with a robust and reliable uncertainty estimate when applied to the ring current.

In the ring current dynamics, various loss mechanisms contribute to the ring current decay, including the loss to the upper atmosphere through particle precipitation. This study implements the field-line curvature (FLC) scattering mechanism in a kinetic ring current model and investigates its role in precipitating ions into the ionosphere. The newly included process is solved via a diffusion equation in the model with associated pitch-angle dependent diffusion coefficients. The simulation results indicate that (1) the FLC scattering process exert mostly on energetic ions above 30 keV on the nightside where the magnetospheric configuration is more stretching. Such ion loss thereafter leads to a faster recovery of the ring current. (2) The FLC-associated ion precipitation mainly occurs in the outer region (L>5 for protons and L>4.5 for oxygen ions) on the nightside, and the oxygen ion precipitation takes places in a wider region than protons although its intensity is much lower. Comparisons with POES observations suggest that more precipitation is needed in the inner region, implying that other loss process is required in the model. (3) We further found that the precipitating energy flux of protons due to the FLC scattering can sometimes become comparable to the one from the electrons on the nightside, although electrons usually dominate the ionospheric energy deposit from the midnight eastward towards the dayside. (4) Finally, the FLC scattering process seems to be capable of explaining the formation of the isotropic boundary in the ionosphere.

During geomagnetic storms, magnetospheric wave activity drives the ion precipitation which can become an important source of energy flux into the ionosphere and strongly affect the dynamics of the Magnetosphere-Ionosphere (MI) coupling. In this study, we investigate the role of Electro Magnetic Ion Cyclotron (EMIC) waves in causing ion precipitation into the ionosphere using simulations from the RAM-SCBE model with and without EMIC waves included. The global distribution of H-band and He-band EMIC wave intensity in the model is based on three different EMIC wave models statistically derived from satellite measurements. Comparisons among the simulations and with observations suggest that the EMIC wave model based on recent Van Allen Probes observations is the best in reproducing the realistic ion precipitation into the ionosphere. Specifically, the maximum precipitating proton fluxes appear at L=4-5 in the afternoon-to-night sector which is in good agreement with statistical results, and the temporal evolution of integrated proton energy fluxes at auroral latitudes is consistent with earlier studies of the stormtime precipitating proton energy fluxes and vary in close relation to the Dst index. Besides, the simulations with this wave model can account for the enhanced precipitation of <20 keV proton energy fluxes at regions closer to earth (L<5) as measured by NOAA/POES satellites, and reproduce reasonably well the intensity of <30 keV proton energy fluxes measured by DMSP satellites. It is suggested that the inclusion of H-band EMIC waves improves the intensity of precipitation in the model leading to better agreement with the NOAA/POES data.