Assessment of Dynamic Mode Decomposition (DMD) Model for Ionospheric TEC Map Predictions

Vlad Landa, Yuval Reuveni

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In this study, we assess the Dynamic Mode Decomposition (DMD) model applied with global ionospheric vertical Total Electron Content (vTEC) maps to construct 24-h global ionospheric vTEC map forecasts using the available International GNSS Service (IGS) 2-h cadence vTEC maps. In addition, we examine the impact of a EUV 121.6 nm time series data source with the DMD control (DMDc) framework, which shows an improvement in the vTEC Root Mean Square Error (RMSE) values compared with the IGS final solution vTEC maps. Both the DMD and DMDc predictions present close RMSE scores compared with the available CODE 1-day predicted ionospheric maps, both for quiet and disturbed solar activity. Finally, we evaluate the predicted global ionospheric vTEC maps with 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 these findings, we argue that the commonly adopted vTEC map comparison RMSE metric fails to correctly reflect an informative impact with L1 single-frequency positioning solutions using dual-frequency ionospheric corrections.

Original languageEnglish
Article number365
JournalRemote Sensing
Issue number2
StatePublished - Jan 2023


  • DMD
  • DMDc
  • Ionospheric VTEC forecasts
  • Machine Learning


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