A data-driven model of Earth’s magnetosheath is developed by training a Bayesian recurrent neural network to reproduce Magnetospheric MultiScale (MMS) measurements of the magnetosheath plasma and magnetic field using measurements from the Wind spacecraft upstream of Earth at the first Earth-Sun Lagrange point (L1). This model, called PRIME-SH in reference to its progenitor algorithm PRIME (Probabilistic Regressor for Input to the Magnetosphere Estimation), is shown to predict spacecraft observations of magnetosheath conditions accurately in a statistical sense with a continuous rank probability score (CRPS) of $0.227\sigma$ and more accurately than current analytical models of the magnetosheath. Furthermore, PRIME-SH is shown to reproduce physics not explicitly enforced during training, such as field line draping, the dayside plasma depletion layer, the magnetosheath flow stagnation point, and the Rankine-Hugoniot MHD shock jump conditions. PRIME-SH has the additional benefits of being computationally inexpensive relative to global MHD simulations, being capable of reproducing difficult-to-model physics such as temperature anisotropy, and being capable of reliably estimating its own uncertainty to within $3.5\%$.

Space-based observations of the signatures associated with STEVE show how this phenomenon might be closely related to an extreme version of a SAID channel. Measurements show high velocities ($>$4km/s), high temperatures ($>$4,000 K), and very large current density drivers (up to 1$\mu$A/m$^2$). This phenomena happens in a small range of latitudes, less than a degree, but with a large longitudinal span. In this study, we utilize the GEMINI model to simulate an extreme SAID/STEVE. We assume a FAC density coming from the magnetosphere as the main driver, allowing all other parameters to adjust accordingly. We have two main objectives with this work: show how an extreme SAID can have velocity values comparable or larger than the ones measured under STEVE, and to display the limitations and missing physics that arise due to the extreme values of temperature and velocity. Changes had to be made to GEMINI due to the extreme conditions, particularly some neutral-collision frequencies. The importance of the temperature threshold at which some collision frequencies go outside their respective bounds, as well as significance of the energies that would cause inelastic collisions and impact ionization are displayed and discussed. We illustrate complex structures and behaviors, emphasizing the importance of 3D simulations in capturing these phenomena. Longitudinal structure is emphasized, as the channel develops differently depending on MLT. However, these simulations should be viewed as approximations due to the limited observations available to constrain the model inputs and the assumptions made to achieve sensible results.

A new model for the shape of the magnetopause is presented using a closed-form analytic function known as a tractrix. This shape is derived from several physics-based underpinnings, eliminating the need for fitting ad-hoc functional forms that, while convenient, are not physically motivated. One feature of the magnetopause predicted by this model is that the magnetotail flares outward until it reaches a constant width, a feature that has significant observational evidence but is seldom represented in functional forms of the magnetopause shape. To optimize the parameters of this model, a dataset of over 13,000 magnetopause crossings from THEMIS/ARTEMIS, Cluster, Geotail, Interball, and several other spacecraft is utilized. Using a Bayesian approach combined with a Markov Chain Monte Carlo (MCMC) method to estimate the posterior probability distribution in parameter space, the maximum likelihood parameters for the model that optimize its performance on this dataset are determined. The modelâ\euro™s performance is compared to that of other popular models of the magnetopause with a focus on their relative performance, and is shown to outperform models that assume the tail flares outward to infinity at far distances. The optimized model more accurately predicts magnetopause position along the tail than other popular static analytic magnetopause models, while still being easy to implement for a variety of applications.