TY  - JOUR
A1  - Smirnov, Artem
A1  - Berrendorf, Max
A1  - Shprits, Yuri Y.
A1  - Kronberg, Elena A.
A1  - Allison, Hayley J.
A1  - Aseev, Nikita
A1  - Zhelavskaya, Irina
A1  - Morley, Steven K.
A1  - Reeves, Geoffrey D.
A1  - Carver, Matthew R.
A1  - Effenberger, Frederic
T1  - Medium energy electron flux in earth's outer radiation
belt (MERLIN)
BT  - a Machine learning model
JF  - Space weather : the international journal of research and applications
N2  - 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.
KW  - machine learning
KW  - radiation belts
KW  - electron flux
KW  - empirical modeling
KW  - magnetosphere
KW  - electrons
Y1  - 2020
U6  - https://doi.org/10.1029/2020SW002532
SN  - 1542-7390
VL  - 18
IS  - 11
PB  - American geophysical union, AGU
CY  - Washington
ER  -