PRIME‐SH: A Data‐Driven Probabilistic Model of Earth's Magnetosheath

Kurzfassung

As the solar wind encounters Earth's magnetosphere and diverts around it, a shock is formed that heats and compresses the plasma and warps the magnetic field frozen into it. This shocked plasma and magnetic field, known as the magnetosheath, is what drives energy transfer at the magnetopause. Due to orbital constraints there is no continuous in‐situ monitor of magnetosheath conditions. Studies of solar wind magnetosphere interaction typically rely on solar wind conditions measured at L1 propagated to Earth by some algorithm, which are then either used directly or used to drive some model of the magnetosheath. This process has numerous points of uncertainty, from the choice of propagation algorithm to the choice of magnetosheath model (or lack thereof). To address these concerns with the traditional approach, this study develops a data‐driven model of the magnetosheath that uses data from L1 as its input. This new model, called Probabilistic Regressor for Input to the Magnetosphere Estimation‐magnetosheath, adapts a recurrent neural network architecture that is capable of estimating uncertainties for its predictions. This new model is verified to be accurate in a statistical sense, and is also capable of representing physics that is not explicitly incorporated in the model during training.