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

ments 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 base- line 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 tempo- ral 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 ap- proaches to ionospheric disturbance detection and prediction in the still understudied North African sector. Author: BOUHADI, Nouhaïla Presenter: BOUHADI, Nouhaïla Session Classification: Poster Session Track Classification: Machine Learning and AI: Machine Learning in Space, Earth & Atmospheric Sciences