Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques
Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt radio communications, and threaten power infrastructure, making accurate forecasting of the high-latitude ionosphere's response a critical operational need. Existing approaches, empirical climatological models and physics-based magnetohydrodynamic simulations, either smooth out the ionosphere's time-dependent, non-linear response or are too computationally expensive for real-time use, while prior machine learning efforts have mostly targeted scalar indices rather than the full spatial structure of ionospheric convection. Here we train and compare three deep learning architectures, a probabilistic...
| Authors | Francesco Pio Ramunno, Simone Mestici, Igino Coco et al. |
|---|---|
| Primary category | physics.space-ph |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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