Papers from SWICo members
Claudio Cesaroni, Luca Spogli, Angela Aragon-Angel, Michele Fiocca, Varuliator Dear, Giorgiana De Franceschi, Vincenzo Romano
We describe a novel empirical model to forecast, one day in advance, the Total Electron Content (TEC) at global scale. The technique is based on the Global Ionospheric Map (GIM), provided by the International GNSS Service (IGS), and exploits a nonlinear autoregressive neural network with external input (NARX) applied to selected TEC time series for soma particular GIM grid points (24 h single-point TEC forecasting), taking into account the actual and forecasted geomagnetic conditions. To extend the forecasting at a global scale, the technique leverages of the NeQuick2 Model fed by an effective sunspot number R12 (R12eff), calculated by minimizing the root mean square error (RMSE) between NARX output and NeQuick2 applied at the same GIM grid points. This new approach is able to reproduce the behavior of the ionosphere especially during disturbed periods.
The performance of the forecasting model is extensively validated under different geospatial conditions, against both TEC maps products by UPC (Universitat Politècnica de Catalunya) and independent TEC data from Jason-3 spacecraft. The validation gives very satisfactory results in terms of RMSE, as it has been found to range 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-h forecasting technique even over the areas poorly covered by GNSS receivers (i.e. the oceans). The validation of the forecasting during five geomagnetic storms reveals that the model performance is not deteriorated during disturbed periods. This 24-h empirical approach is currently implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users in order to support the mitigation of the ionospheric effects on GNSS based technologies.
Publication: Cesaroni, C., Spogli, L., Aragon-Angel, A., Fiocca, M., Dear, V., De Franceschi, G., & Romano, V. (2020). Neural network based model for global Total Electron Content forecasting. Journal of Space Weather and Space Climate, 10, 11.