Radiation Belt Model Including Semi‐Annual Variation and Solar Driving (Sentinel)

Abstract

AbstractThe Earth's outer radiation belt response to geospace disturbances is extremely variable spanning from a few hours to several months. In addition, the numerous physical mechanisms, which control this response depend on the electron energy, the timescale, and the various types of geospace disturbances. As a consequence, various models that currently exist are either specialized, orbit‐specific data‐driven models, or sophisticated physics‐based ones. In this paper, we present a new approach for radiation belt modeling using Machine Learning methods driven solely by Solar wind speed and pressure, Solar flux at 10.7 cm, and the angle controlling the Russell‐McPherron effect (θRM). We show that the model can successfully reproduce and predict the electron fluxes of the outer radiation belt in a broad energy (0.033–4.062 MeV) and L‐shell (2.5–5.9) range, and moreover, it can capture the long‐term modulation of the semi‐annual variation. We also show that the model can generalize well and provide successful predictions, even outside of the spatio‐temporal range it has been trained with, using 0.8 MeV electron flux measurements from GOES‐15/EPEAD at a geostationary orbit.