loading page

PRIME-SH: A Data-Driven Probabilistic Model of Earth’s Magnetosheath
  • +4
  • Connor O'Brien,
  • Brian Walsh,
  • Ying Zou,
  • Ramiz A. Qudsi,
  • Samira Tasnim,
  • Huaming Zhang,
  • David Gary Sibeck
Connor O'Brien
Boston University

Corresponding Author:[email protected]

Author Profile
Brian Walsh
Boston University
Author Profile
Ying Zou
Johns Hopkins University Applied Physics Laboratory
Author Profile
Ramiz A. Qudsi
Boston University
Author Profile
Samira Tasnim
German Aerospace Center (DLR)
Author Profile
Huaming Zhang
University of Alabama in Huntsville
Author Profile
David Gary Sibeck
GSFC
Author Profile

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 $3.5\%$.
24 Apr 2024Submitted to ESS Open Archive
26 Apr 2024Published in ESS Open Archive