Forecasting Megaelectron-Volt Electrons inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms

TL;DR

PreMevE 2.0 is an advanced machine learning-based model that significantly improves forecasting of MeV electrons in Earth's outer radiation belt by incorporating solar wind data, achieving reliable 1-2 day predictions.

Contribution

The paper introduces PreMevE 2.0, a new model that enhances electron belt forecasts using supervised machine learning and upstream solar wind parameters, with demonstrated high accuracy.

Findings

Linear regression often outperforms other models, indicating linear relationships dominate electron dynamics.

PreMevE 2.0 achieves ~0.87 (1-day) and ~0.82 (2-day) forecast efficiency.

The model can predict the onset of MeV electron events within 2 days.

Abstract

Here we present the recent progress in upgrading a predictive model for Megaelectron-Volt (MeV) electrons inside the Earth's 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 model's 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.