Boynton, R.J. orcid.org/0000-0003-3473-5403, Aryan, H., Dimmock, A.P. et al. (1 more author) (2020) System identification of local time electron fluencies at geostationary orbit. Journal of Geophysical Research: Space Physics, 125 (11). ISSN 2169-9380
Abstract
The electron fluxes at geostationary orbit measured by Geostationary Operational Environmental Satellite (GOES) 13, 14, and 15 spacecraft are modeled using system identification techniques. System identification, similar to machine learning, uses input‐output data to train a model, which can then be used to provide forecasts. This study employs the nonlinear autoregressive moving average exogenous technique to deduce the electron flux models. The electron fluxes at geostationary orbit are known to vary in space and time, making it a spatiotemporal system, which complicates the modeling using system identification/machine learning approach. Therefore, the electron flux data are binned into 24 magnetic local time (MLT), and a separate model is developed for each of the 24 MLT bins. MLT models are developed for six of the GOES 13, 14, and 15 electron flux energy channels (75 keV, 150 keV, 275 keV, 475 keV, >800 keV, and >2 MeV). The models are assessed on separate test data by prediction efficiency (PE) and correlation coefficient (CC) and found these to vary by MLT and electron energy. The lowest energy of 75 keV at the midnight sector had a PE of 36.0 and CC of 59.3, which increased on the dayside to a PE of 66.9 and CC of 81.6. These metrics increased to the >2 MeV model, which had a low PE and CC of 63.0 and 81.8 on the nightside to a high of 80.3 and 90.8 on the dayside.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ |
Keywords: | radiation belts; forecast; electron flux; machine learning; system identification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Natural Environment Research Council NE/P017061/1 Science and Technology Facilities Council ST/R000697/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Dec 2020 14:43 |
Last Modified: | 09 Dec 2020 14:43 |
Status: | Published |
Publisher: | American Geophysical Union (AGU) |
Refereed: | Yes |
Identification Number: | 10.1029/2020ja028262 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168790 |