@article{WalkerBoyntonShpritsetal.2022,
  author    = {Walker, Simon N. and Boynton, Richard J. and Shprits, Yuri Y. and Balikhin, Michael A. and Drozdov, Alexander},
  title     = {Forecast of the energetic electron environment of the radiation belts},
  series = {Space Weather: The International Journal of Research and Applications},
  volume    = {20},
  journal   = {Space Weather: The International Journal of Research and Applications},
  number    = {12},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1542-7390},
  doi       = {10.1029/2022SW003124},
  pages     = {21},
  year      = {2022},
  abstract  = {Different modeling methodologies possess different strengths and weakness. For instance, data based models may provide superior accuracy but have a limited spatial coverage while physics based models may provide lower accuracy but provide greater spatial coverage. This study investigates the coupling of a data based model of the electron fluxes at geostationary orbit (GEO) with a numerical model of the radiation belt region to improve the resulting forecasts/pastcasts of electron fluxes over the whole radiation belt region. In particular, two coupling methods are investigated. The first assumes an average value for L* for GEO, namely LGEO* L-GEO* = 6.2. The second uses a value of L* that varies with geomagnetic activity, quantified using the Kp index. As the terrestrial magnetic field responds to variations in geomagnetic activity, the value of L* will vary for a specific location. In this coupling method, the value of L* is calculated using the Kp driven Tsyganenko 89c magnetic field model for field line tracing. It is shown that this addition can result in changes in the initialization of the parameters at the Versatile Electron Radiation Belt model outer boundary. Model outputs are compared to Van Allen Probes MagEIS measurements of the electron fluxes in the inner magnetosphere for the March 2015 geomagnetic storm. It is found that the fixed LGEO* L-GEO* coupling method produces a more realistic forecast.},
  language  = {en}
}