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Here we present the recent progress in upgrading a predictive model for Megaelectron-Volt (MeV) electrons inside the Earths outer Van Allen belt. This updated model, called PreMevE 2.0, is demonstrated to make much improved forecasts, particularly at outer Lshells, by including upstream solar wind speeds to the models input parameter list. Furthermore, based on several kinds of linear and artificial machine learning algorithms, a list of models were constructed, trained, validated and tested with 42-month MeV electron observations from Van Allen Probes. Out-of-sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1-day (2-day) forecasts with Lshell-averaged performance efficiency values ~ 0.87 (~0.82). Interestingly, the linear regression model is often the most successful one when compared to other models, which indicates the relationship between 1 MeV electron dynamics and precipitating electrons is dominated by linear components. It is also shown that PreMevE 2.0 can reasonably predict the onsets of MeV electron events in 2-day forecasts. This improved PreMevE model is driven by observations from longstanding space infrastructure (a NOAA satellite in low-Earth-orbit, the solar wind monitor at the L1 point, and one LANL satellite in geosynchronous orbit) to make high-fidelity forecasts for MeV electrons, and thus can be an invaluable space weather forecasting tool for the future.
Energetic electrons inside Earths 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-vo
Real-time prediction of the dynamics of energetic electrons in Earths radiation belts incorporating incomplete observation data is important to protect valuable artificial satellites and to understand their physical processes. Traditionally, reduced
Energetic particle fluxes in the outer magnetosphere present a significant challenge to modelling efforts as they can vary by orders of magnitude in response to solar wind driving conditions. In this article, we demonstrate the ability to propagate t
The Cluster mission, launched in 2000, has produced a large database of electron flux intensity measurements in the Earths magnetosphere by the Research with Adaptive Particle Imaging Detector (RAPID)/ Imaging Electron Spectrometer (IES) instrument.
The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review