摘要：Extended periods of relativistic electron intensity at geosynchronous orbit can create severe deep-charging hazards for satellites. Over the last 20 years a number of models have been developed to predict electron flux levels using solar wind and geomagnetic indices as inputs. We analyze the results of several of these including the Relativistic Electron Forecast Model based on the linear prediction filter technique, a neural network algorithm, and the physics-based diffusion method. Analyses using the methods of simple persistence and recurrence (based on the 27 day solar rotation) are also included as performance baselines. Comparisons are made to the GOES > 2 MeV electron flux to determine which model or method gives the best +1, +2, and +3 day forecasts of average daily flux during the interval 1996–2008. Model inputs include combinations of ΣKp, the daily average solar wind speed, and daily average > 2 MeV electron fluxes for one day or multiple days prior to the forecast days of interest. Prediction efficiencies are calculated for 6 month intervals. After evaluating all the models, there was no clear winner; each model did well at different phases of the solar cycle. All models perform their best during the inclining phase of solar minimum but not as well during solar maximum and the declining phase of solar minimum. While persistence is respectable for +1 day prediction, models clearly give superior +2 and +3 day predictions and should be used to obtain those forecasts.
