Search

The fast Van Allen radiation belt electron dynamics during geomagnetic storms have not yet been fully explained, in part due to limitations of standard satellite missions in both orbit and the number of spacecraft. Here we overcome these limitations using measurements from the Global Positioning System (GPS) constellation during an acceleration event on 26 August 2018. We show that the acceleration of relativistic electrons occurs in two distinct bursts, each dominated by a different acceleration mechanism. The first burst enhances the radiation belt electrons by four orders of magnitude in 2 hr and is consistent with ULF‐wave radial diffusion. The second burst is likely caused by the local acceleration and delivers an order‐of‐magnitude increase in 20 min. This work demonstrates how distributed, operational measurements can be used to resolve phenomena not observable with previous capabilities, and that rapid energization of the radiation belt can occur much faster than previously reported. Plain Language Summary: In this paper, we present a detailed analysis of terrestrially‐trapped electron space radiation during the August 2018 geomagnetic storm. This event is characterized by a very fast enhancement in the trapped electron population that increases particle counts by more than a factor of a thousand over only 6 hr. Such fast dynamics cannot be resolved by typical survey missions due to their long orbital periods. We instead use measurements from 20 satellites in the Global Positioning System (GPS) constellation, which allows us to perform an analysis of the space radiation dynamics on much shorter timescales. These GPS data reveal that the fast enhancement during the August 2018 storm occurred in two distinct bursts. By introducing a novel technique for GPS particle data analysis, we also determine that each of the bursts is governed by different physical processes that act on different timescales. The revealed fast dynamics of near‐Earth trapped radiation point toward a need to reevaluate the classic paradigm that the changes in the radiation levels are slow and can be revealed by surveys with a low number of spacecraft. Indeed, we foresee a critical role for constellation measurements, such as from GPS, in the future of radiation belt science. Key Points: Constellation measurements of the Van Allen radiation belt electrons can be used to reveal fast nonadiabatic changes at sub‐orbit timescalesElectron acceleration during the August 2018 storm consists of two distinct acceleration bursts governed by different physical processesULF‐wave radial diffusion and local acceleration can significantly alter radiation belt electron content on timescales of minutes to hours [ABSTRACT FROM AUTHOR]

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. Key Points: A machine learning model is created to predict electron flux at MEO for energies 120–600 keVThe model requires solar wind parameters and geomagnetic indices as input and does not use persistenceMERLIN model yields high accuracy and high correlation with observations (0.8) [ABSTRACT FROM AUTHOR]