Radiation belt model including semi-annual variation and Solar driving (SENTINEL)

The 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 time-scale and the types of geospace disturbances. As a consequence, the 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 modelling using Machine Learning methods driven solely by solar wind speed and pressure, Solar flux at 10.7 cm and the Russell-McPherron angle. We use Van Allen Probes data to train our model and show that it 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 present validation studies of the model’s performance using data from other missions which are outside the spatio-temporal training regime such as the E>0.8 MeV electron flux measurements from GOES-15/EPEAD at geostationary orbit.

This work has received funding from the European Union’s Horizon 2020 research and innovation programme "SafeSpace" under grant agreement No 870437 and from the European Space Agency under the "European Contribution to International Radiation Environment Near Earth (IRENE) Modelling System" activity under ESA Contract No 4000127282/19/NL/IB/gg.