Global Ionospheric Vertical Total Electron Content (VTEC) map predictions using Dynamic Mode Decomposition (DMD)

The obtainability of past and current global Ionosphere vertical TEC maps, estimated from dual-frequency Global Navigation Satellite Systems (GNSS) signals, has drawn the attention of big data and Machine Learning (ML) scientists, to seek for more accurate forecasts while investigating various ML methods as well as the physical based models. Here, we evaluate the use of the Dynamic Mode Decomposition (DMD) model, combined with global ionospheric vertical Total Electron Content (VTEC) maps for constructing daily global Ionospheric VTEC map predictions using 2-hours temporal resolution maps. Furthermore, we also assess the influence of adding additional data source, in the form of EUV time series, with the DMD control (DMDc) framework. This result as a decrease in the VTEC Root Mean Square Error (RMSE) values when compared with the International GNSS Service (IGS) final VTEC products. Additionally, Both the DMD and DMDc forecasts achieve comparable RMSE scores versues the available CODE'S 1-day predicted ionospheric maps, both for quiet and disturbed solar activity periods. As a last step, we also estimate our global Ionospheric VTEC maps predictions using the East-North-Up (ENU) coordinate system errors metric, as an ionospheric correction source for L1 single frequency GPS/GNSS Single Point Positioning (SPP) solutions. Based on this study, we conclude that the frequently used VTEC map comparison RMSE metric forsakes to adequately reveal an informative impact using L1 single frequency positioning solutions augmented by a dual frequency VTEC ionospheric map correction.