Abstract
AbstractThe 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.
Highlights
The energetic electrons in the radiation belt are a particular interest to space weather as they are known to cause damage to spacecraft that transit the region (Baker et al, 1987)
These use input‐output data to automatically deduce a model using a machine learning algorithm. One such model was developed by Boynton et al (2015) using a method based on Nonlinear AutoRegressive Moving Average eXogenous (NARMAX) models that forecasts the daily averaged >2 MeV electron fluxes at GEO, similar to the Relativistic Electron Forecast Model (REFM)
The aim of this paper is to develop similar electron flux models to those by Boynton et al (2019), but for the other energies provided by the Geostationary Operational Environmental Satellite (GOES) 13, 14, and 15 spacecraft
Summary
The energetic electrons in the radiation belt are a particular interest to space weather as they are known to cause damage to spacecraft that transit the region (Baker et al, 1987). Physics‐based numerical models have become possible for real‐time operations such as the British Antarctic Survey Radiation Belt Model (BAS‐RBM) (Horne et al, 2013). Another method to model the radiation belts that is becoming increasingly popular is based on machine learning/system identification. These use input‐output data to automatically deduce a model using a machine learning algorithm One such model was developed by Boynton et al (2015) using a method based on Nonlinear AutoRegressive Moving Average eXogenous (NARMAX) models that forecasts the daily averaged >2 MeV electron fluxes at GEO, similar to the REFM. One issue with REFM and Boynton et al (2015) models
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