Modeling and Prediction of Near-Earth Plasma Sheet Parameters Using the Rice Convection Model and the Recurrent Neural Network

Interaction between Earth’s magnetotail and its inner magnetosphere plays an important role in the transport of mass and energy in the ionosphere–magnetosphere coupled system. A number of first-principles models are devoted to understanding the associated dynamics. However, running these models, including both magnetohydrodynamic models and kinetic drift models, can be computationally expensive when self-consistency and high spatial resolution are required. In this study, we exploit an approach of building a parallel statistical model, based on the long short-term memory (LSTM) type of recurrent neural network, to forecast the results of a first-principles model, called the Rice Convection Model (RCM). The RCM is used to simulate the transient injection events, in which the flux-tube entropy parameter, dawn-to-dusk electric field component, and cumulative magnetic flux transport are calculated in the central plasma sheet. These key parameters are then used as initial inputs for training the LSTM. Using the trained LSTM multivariate parameters, we are able to forecast the plasma sheet parameters beyond the training time for several tens of minutes that are found to be consistent with the subsequent RCM simulation results. Our tests indicate that the recurrent neural network technique can be efficiently used for forecasting numerical simulations of magnetospheric models. The potential to apply this approach to other models is also discussed.

Interplay of bubble injections in the plasma sheet dynamics as inferred from RCM-I simulations

<p><span>Understanding the transport of hot plasma from tail towards the inner magnetosphere is of great importance to improve our perception of the near-Earth space environment. In accordance with the recent observations, the contribution of bursty bulk flows (BBFs)/bubbles in the inner plasma sheet especially in the storm-time ring current formation is nonnegligible. These high-speed plasma flows with depleted flux tube/entropy are likely formed in the mid tail due to magnetic reconnection and injected earthward as a result of interchange instability. In this presentation, we investigate the interplay of these meso-scale structures on the average magnetic field and plasma distribution in various regions of the plasma sheet, using the Inertialized Rice Convection Model (RCM-I). We will discuss the comparison of our simulation results with the observational statistics and data-based empirical models.</span></p>