Abstract:

The convection of the Earth’s ionospheric plasma maps the large-scale convective circulation of the magnetosphere and the convective electric field in the polar ionosphere that is driven by the interaction between the solar wind and the Earth’s magnetic field; it is closely related to the coupling system between the interplanetary magnetic field and Earth’s magnetic field. SuperDARN is a radar network for the study of space weather phenomena in the Earth’s magnetosphere. It is an important source of polar ionospheric convection velocity data and is useful for studying ionospheric convection at mid- to high-latitudes. This paper employed the two-dimensional, fitted ionospheric convective velocity data obtained from 20 high-frequency coherent scattering radars in the SuperDARN network in the northern hemisphere in 2015 and 2016. Using the BP-Adaboost model, the FC-GRU model, and the ED-ConvLSTM spatiotemporal sequence model, a two-dimensional global prediction model of ionospheric convection velocity for 2 minutes was developed. Model input includes the three components of the interplanetary magnetic field, the six near-Earth space parameters of solar wind speed, solar wind density, and geomagnetic index, as well as the longitude and latitude corresponding to the echo point. Two-dimensional convection velocity is the model output. Independent datasets were used to evaluate the performance of the BP-Adaboost, FC-GRU, and ED-ConvLSTM spatiotemporal sequence models on the basis of the Root Mean Square Error (E_RMSE) and Mean Absolute Error (E_MAE) between predicted and measured values. The coefficient of determination R² was introduced to compare the performance of the three models. The results show that BP-Adaboost, the neural network model based on ensemble learning, has the smallest prediction error: (1) the E_RMSE for predictions of the magnitude and direction of convection velocity are 119.62 m·s^–1 and 62.23°, respectively; (2) the E_MAE for predictions of the magnitude and direction of convection velocity are 75.70 m·s^–1 and 41.80°, respectively; (3) the R² for predictions of magnitude and direction are 0.70 and 0.59, respectively. Relative to the FC-GRU and ED-ConvLSTM models, the performance of the BP-Adaboost model in predicting ionospheric convection velocity is superior in terms of E_RMSE, E_MAE, and R² values for both convection velocity magnitude and direction.

Key words:

polar ionospheric convection, model construction and prediction, deep learning, SuperDARN radar