Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

Abstract

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis. Plain Language Summary The radiation belts of the Earth, which are the zones of charged energetic particles trapped by the geomagnetic field, comprise complex and dynamic systems posing a significant threat to a variety of commercial and military satellites. While the inner belt is relatively stable, the outer belt is highly variable and depends substantially on solar activity; therefore, accurate and improved models of electron flux in the outer radiation belt are essential to understand the underlying physical processes. Although many models have been developed for the geostationary orbit and relativistic energies, prediction of electron flux in the 120-600 keV energy range still remains challenging. We present a data-driven model of the medium energies (120-600 keV) differentialelectron flux in the outer radiation belt based on machine learning. We use 17 years of electron observations by Global Positioning System (GPS) satellites. We set up a 3D model for flux prediction in terms of L-values, MLT, and magnetic latitude. The model gives reliable predictions of the radiation environment in the outer radiation belt and has wide space weather applications.

Intercalibration of the Plasma Density Measurements in Earth's Topside Ionosphere

“…Another metric evaluated for the conjunction analysis is the correlation between the data sets. It has been demonstrated that the linear Pearson correlation can be affected by data noise, whereas the Spearman rank correlation is a more robust metric in the presence of outliers (e.g., Smirnov et al., 2020). The value of the Spearman correlation for GRACE‐COSMIC comparison is high (0.96), also illustrating that the two data sets closely agree with each other.…”

Energetic Charged Particles in the Terrestrial Magnetosphere: Cluster/RAPID Results

“…The combined data set thus covers more than two solar cycles. It can be useful for the long-term statistical analysis of electron flux behavior in the outer radiation belt, and can be incorporated into the empirical models of the medium energy electron flux, for example, by Smirnov, Berrendorf, et al (2020). Moreover, the electron fluxes converted to PSD can be used to set up boundary conditions for radiation belts modeling, as well as for data assimilation purposes.…”

Evidence of Sub‐MeV EMIC‐Driven Trapped Electron Flux Dropouts From GPS Observations

“…The public data product (Morley et al, 2017) provides differential omnidirectional flux values at 15 energies from 120 keV up to 10 MeV, which we use in this study. For more information, including fit quality checks, see Smirnov et al (2020) and references therein.…”