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System Identification of Local Time Electron Fluencies at Geostationary Orbit
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- Boynton, R. J. (author)
- Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield, S Yorkshire, England
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- Aryan, H. (author)
- Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield, S Yorkshire, England; Univ Calif Los Angeles, Dept Atmospher & Ocean Sci, Los Angeles, CA USA
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- Dimmock, Andrew P. (author)
- Uppsala universitet,Institutet för rymdfysik, Uppsalaavdelningen
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- 2020
- 2020
- English.
- In: Journal of Geophysical Research - Space Physics. - 2169-9380 .- 2169-9402. ; 125:11
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Fysik -- Astronomi, astrofysik och kosmologi (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Astronomy, Astrophysics and Cosmology (hsv//eng)
- NATURVETENSKAP -- Fysik -- Fusion, plasma och rymdfysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Fusion, Plasma and Space Physics (hsv//eng)
Keyword
- radiation belts
- forecast
- electron flux
- machine learning
- system identification
Publication and Content Type
- ref (subject category)
- art (subject category)
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