Neural Network Based Model for Global Total Electron Content Forecasting

We introduce a novel empirical model to forecast, 24 hours in advance, the Total Electron Content (TEC) at planetary scale. The technique leverages on the Global Ionospheric Map (GIM), provided by the International GNSS Service (IGS), and applies a nonlinear autoregressive neural network with external input (NARX) to selected GIM grid points for the 24 hours single-point TEC forecasting, taking into account the actual and forecasted geomagnetic conditions. To extend the forecasting at a planetary scale, the technique makes use of the NeQuick2 Model fed by an effective sunspot number R12 (R12eff), estimated by minimizing the root mean square error (RMSE) between NARX output and NeQuick2 applied at the same GIM grid points. The novel approach is able to reproduce the features of the planetary ionosphere especially during disturbed periods. The performance of the forecasting approach is extensively tested under different geospatial conditions, against both TEC maps products by UPC (Universitat Politècnica de Catalunya) and independent TEC data from Jason-3 spacecraft. The testing results are very satisfactory in terms of RMSE ranges between 3 and 5 TECu. RMSE depend on the latitude sectors, time of the day, geomagnetic conditions, and provide a statistical estimation of the accuracy of the 24-hours forecasting technique even over the oceans. The validation of the forecasting during 5 geomagnetic storms reveals the very satisfactory results of the technique especially during disturbed periods. This 24-hours empirical approach is currently implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users.