Abstract: The Cross Polar Cap electric field (i.e. the ionospheric convective electric field) was calculated using ionospheric potential data (i.e. the Cross Polar Cap potential) from the Super Dual Auroral Radar Network (SuperDARN). Then, historical data for the convective electric field were introduced and the ionospheric electric field model was constructed using a multivariate linear regression algorithm and a back propagation neural network algorithm that used ionospheric electric field data from 2014. The accuracy and stability of the two models were verified using an independent dataset. The results show that the root mean square of the error between the model values and the measured values is in the range of 2.0 mV·m^–1 to 3.5 mV·m^–1, the mean absolute error is in the range of 1.5 mV·m^–1 to 3.0 mV·m^–1, and the linear correlation coefficient is greater than 0.6 and has a maximum of 0.9. At the same time, the historical data of convection electric field in the first 20 min were introduced as the input for the multivariate linear regression model and BP neural network model. These results show that the back propagation neural network model has better prediction performance than the multivariate linear regression model.

Key words: SuperDARN radar, ionospheric electric field, multivariate linear regression model, BP neural network model