Quantifying the Relative Contributions of Geomagnetic, Stratospheric, and Solar Drivers to Ionospheric TEC Variability: A Composite and Machine-Learning Approach
Keywords:
Total Electron Content (TEC); Driver Attribution; Sudden Stratospheric Warming (SSW); Geomagnetic Storms; Interpretable Machine Learning.Abstract
The ionosphere's Total Electron Content (TEC) is modulated by distinct natural drivers, yet conventional models often fail to separate their superimposed effects. This study introduces a unified attribution framework combining composite analysis with interpretable machine learning to quantify the relative contributions of geomagnetic (G), stratospheric (S), and solar (F) drivers to TEC variability. Using multi-station GNSS data from 2015–2025, we apply superposed epoch analysis and a grouped Random Forest model with SHAP (SHapley Additive exPlanations) decomposition. Results reveal a clear latitudinal ordering of driver dominance: the stratospheric group dominates at equatorial and low latitudes during winter, peaking 6–10 days after sudden stratospheric warming (SSW) onset. This signal aligns precisely with semidiurnal (12 h and 12.42 h) tidal amplification identified via wavelet analysis, confirming E-region dynamo coupling. The geomagnetic group dominates mid- to high latitudes during storm main phases, rising sharply at SYM-H minimum and decaying within 24–48 hours. The solar group is confined to the dayside, exhibiting a short-duration pulse (10–30 minutes) aligned with X-ray flux peaks. Cross-validation confirms that SHAP-derived temporal and spatial signatures match independent composite analysis, proving the model captures physical mechanisms rather than spurious correlations. Furthermore, analysis of compound events (SSW concurrent with moderate storms) demonstrates nonlinear superposition, producing longitude- and local-time-dependent responses that linear models cannot reproduce. This framework provides a physically grounded approach to driver attribution, offering a diagnostic tool for improving TEC forecasting models during complex space weather events.
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Copyright (c) 2026 Belay Sitotaw Goshu (Author)
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