Reconstruction of electron radiation belts using data assimilation and machine learning

Frontiers in Astronomy and Space Sciences

Astronomy and Space Sciences

Dmitri Kondrashov¹  Alexander Y. Drozdov²  Yuri Y. Shprits³  Kirill Strounine⁴ 

[1] Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, United States;Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, Los Angeles, CA, United States;Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, Los Angeles, CA, United States;Space Sciences Innovations Inc., Seattle, WA, United States;GFZ German Centre for Geosciences, Potsdam, Germany;Space Sciences Innovations Inc., Seattle, WA, United States;

关键词: radiation belts;    neural network;    multiple linear regression;    VERB code;    data assimilation;    machine learning;   

DOI  :  10.3389/fspas.2023.1072795

 received in 2022-10-17, accepted in 2023-05-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

We present a reconstruction of radiation belt electron fluxes using data assimilation with low-Earth-orbiting Polar Orbiting Environmental Satellites (POES) measurements mapped to near equatorial regions. Such mapping is a challenging task and the appropriate methodology should be selected. To map POES measurements, we explore two machine learning methods: multivariate linear regression (MLR) and neural network (NN). The reconstructed flux is included in data assimilation with the Versatile Electron Radiation Belts (VERB) model and compared with Van Allen Probes and GOES observations. We demonstrate that data assimilation using MLR-based mapping provides a reasonably good agreement with observations. Furthermore, the data assimilation with the flux reconstructed by NN provides better performance in comparison to the data assimilation using flux reconstructed by MLR. However, the improvement by adding data assimilation is limited when compared to the purely NN model which by itself already has a high performance of predicting electron fluxes at high altitudes. In the case an optimized machine learning model is not possible, our results suggest that data assimilation can be beneficial for reconstructing outer belt electrons by correcting errors of a machine learning based LEO-to-MEO mapping and by providing physics-based extrapolation to the parameter space portion not included in the LEO-to-MEO mapping, such as at the GEO orbit in this study.

【 授权许可】

Unknown   
Copyright © 2023 Drozdov, Kondrashov, Strounine and Shprits.