  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.
