Relativistic electron flux forecast at geostationary orbit using Kalman filter based on multivariate autoregressive model

The relativistic electron population at MeV energy in the Van Allen radiation belts at geostationary orbit largely varies in association with solar wind disturbances. To provide alerts of possible satellite malfunctions due to deep‐dielectric charging during relativistic electron enhancements, the National Institute of Information and Communications Technology, Japan, developed an algorithm to forecast daily >2 MeV electron flux variations at geostationary orbit using a multivariate autoregressive model. We examined model accuracy by using solar wind speed, north‐south component of the magnetic field, and dynamic pressure by inputting them as explanatory variates. The results showed that a combination of all three variates was most effective in reducing the prediction error. We focus here on the four‐variate autoregressive model and handle it using the Kalman filter. The time evolution of the forecast is given by the conditional normal distribution: the peak value of forecast probability and the error range. The error range estimation is useful for users who utilize forecasts for operation of the satellites. We investigated the prediction efficiency of +1 day forecasts by evaluating forecast and observation data for a whole solar cycle (1999–2008) every 2 years. The prediction efficiency maintained at more than 69% throughout the solar cycle, although it depends on the phase of the cycle. Comparisons of the prediction efficiencies revealed that our model exhibited the best performance of conventional forecast models, particularly in solar active periods.