GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather

GNSS-TEC Characterization of the 17 March 2015 Geomagnetic Storm over Morocco as a Benchmark for Data-Driven Space Weather Monitoring

Aug 20, 2026, 9:40 AM

20m

Fernando de Mendonça - LIT (National Institute for Space Research, São José dos Campos, SP, Brazil)

Oral Machine Learning in Space, Earth & Atmospheric Sciences Oral Contributions

Speaker

Nouhaïla Bouhadi

Description

We investigated the ionospheric response to the 17 March 2015 geomagnetic storm, the most severe event of solar cycle 24 (SYM-H = -233 nT), using GPS-derived total electron content (TEC) observations from the RABT station in Morocco (34°N, 7°W). Vertical TEC (VTEC) was derived from dual-frequency GPS measurements using differential code bias corrections and a 30° elevation cutoff. The results reveal a strong storm-time enhancement, with ΔVTEC reaching +45.78 TECU, corresponding to a 158% increase above the quiet-day baseline of 28.92 TECU. Lagged correlation analysis shows the strongest relationship with Kp at approximately 3 hours delay (r ≈ 0.53), indicating structured ionospheric variability linked to geomagnetic forcing. The temporal evolution is consistent with rapid electrodynamic effects during the main phase and composition-related changes during the recovery phase. This study highlights the usefulness of GNSS-TEC time series for regional space weather monitoring and provides a physically interpretable benchmark for future machine-learning approaches to ionospheric disturbance detection and prediction in the still understudied North African sector.