Abstract
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 $3.5\%$.