PreMevE-MEO: Predicting Ultra-relativistic Electrons Using Observations from GPS Satellites

TL;DR

This paper introduces PreMevE-MEO, a neural network model that predicts ultra-relativistic electron fluxes in the outer radiation belt using GPS satellite data, enhancing space weather forecasting capabilities.

Contribution

The paper presents a novel predictive model combining CNNs and transformers trained on MEO satellite data, enabling high-fidelity, hourly nowcasts of electron fluxes in the outer radiation belt.

Findings

PreMevE-MEO achieves high correlation with observed electron fluxes.

The model provides hourly nowcasts with high efficiency.

It demonstrates the feasibility of using MEO satellite data for space weather prediction.

Abstract

Ultra-relativistic electrons with energies greater than or equal to two megaelectron-volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium-Earth-orbits (MEO), is called PREdictive MEV Electron (PreMevE)-MEO model that nowcasts ultra-relativistic electron flux distributions across the whole outer belt. Model inputs include above 2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi-Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long-term in-situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model.It is shown that PreMevE-MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations.This prototype PreMevE-MEO model demonstrates the feasibility of making high-fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.