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


AuthorSinha, Saurabh; Chen, Yue; Lin, Youzuo; de Lima, Rafael;
KeywordsSupervised Machine Learning; Van Allen electron radiation belt; Predicting ultra-relativistic electrons; Van Allen Probes
AbstractAbstract Energetic electrons inside Earth’s Van Allen belts pose a major radiation threat to space-borne electronics that often play vital roles in modern society. Ultra-relativistic electrons with energies greater than or equal to two Megaelectron-volt (MeV) are of particular interest, and thus forecasting these ≥2 MeV electrons has significant meaning to all space sectors. Here we update the latest development of the predictive model for MeV electrons in the outer radiation belt. The new version, called PreMevE-2E, forecasts ultra-relativistic electron flux distributions across the outer belt, with no need for in-situ measurements of the trapped MeV electron population except at geosynchronous (GEO) orbit. Model inputs include precipitating electrons observed in low-Earth-orbits by NOAA satellites, upstream solar wind speeds and densities from solar wind monitors, as well as ultra-relativistic electrons measured by one Los Alamos GEO satellite. We evaluated 32 supervised machine learning models that fall into four different classes of linear and neural network architectures, and 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 outperforms individual models at most L-shells, and this PreMevE-2E model can provide 25-hr (∼1-day) and 50-hr (∼2-day) forecasts with high mean performance efficiency and correlation values. Our results also suggest this new model is dominated by nonlinear components at L-shells < ∼4 for ultra-relativistic electrons, different from the dominance of linear components for 1 MeV electrons as previously discovered.
Year of Publication2021
JournalSpace Weather
Volumen/a
Number of Pagese2021SW002773
Section
Date Published08/2021
ISBN
URLhttps://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021SW002773
DOIhttps://doi.org/10.1029/2021SW002773