PreMevE Update: Forecasting Ultra-relativistic Electrons inside Earth's Outer Radiation Belt

TL;DR

This paper presents PreMevE-2E, a machine learning ensemble model that forecasts ultra-relativistic electron fluxes in Earth's outer radiation belt with high accuracy, using minimal in-situ measurements and multiple data sources.

Contribution

The paper introduces a new ensemble machine learning model for predicting >=2 MeV electron fluxes in Earth's outer radiation belt, improving forecast reliability and understanding non-linear dynamics.

Findings

Ensemble models outperform individual models in electron flux forecasting.

The model achieves 25- and 50-hour forecast accuracy with high performance efficiency.

Non-linear components dominate at low L-shells for ultra-relativistic electrons.

Abstract

Energetic electrons inside Earth's outer Van Allen belt pose a major radiation threat to space-borne electronics that often play vital roles in our modern society. Ultra-relativistic electrons with energies greater than or equal to two Megaelectron-volt (MeV) are of particular interest due to their high penetrating ability, and thus forecasting these >=2 MeV electron levels has significant meaning to all space sectors. Here we update the latest development of the predictive model for MeV electrons inside the Earth's outer radiation belt. The new version, called PreMevE-2E, focuses on forecasting ultra-relativistic electron flux distributions across the outer radiation belt, with no need of in-situ measurements except for at the geosynchronous (GEO) orbit. Model inputs include precipitating electrons observed in low-Earth-orbits by NOAA satellites, upstream solar wind conditions (speeds and densities) from solar wind monitors, as well as ultra-relativistic electrons measured by one Los Alamos GEO satellite. We evaluated a total of 32 supervised machine learning models that fall into four different classes of linear and neural network architectures, and also successfully tested ensemble forecasting by using groups of top-performing models. All models are individually trained, validated, and tested by in-situ electron data from NASA's Van Allen Probes mission. It is shown that the final ensemble model generally outperforms individual models overs L-shells, and this PreMevE-2E model provides reliable and high-fidelity 25-hr (~1-day) and 50-hr (~2-day) forecasts with high mean performance efficiency values. Our results also suggest this new model is dominated by non-linear components at low L-shells (< ~4) for ultra-relativistic electrons, which is different from the dominance of linear components at all L-shells for 1 MeV electrons as previously discovered.