{"ID":2839310,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15004","arxiv_id":"2511.15004","title":"IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics","abstract":"The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired model tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global Total Electron Content (TEC), integrating diverse physical drivers and observational datasets. Validating on held-out storm-time and quiet conditions highlights improved skill compared to persistence. By unifying heterogeneous data with scalable graph-based spatiotemporal learning, IonCast demonstrates how machine learning can augment physical understanding of ionospheric variability and advance operational space weather resilience.","short_abstract":"The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning m...","url_abs":"https://arxiv.org/abs/2511.15004","url_pdf":"https://arxiv.org/pdf/2511.15004v1","authors":"[\"Halil S. Kelebek\",\"Linnea M. Wolniewicz\",\"Michael D. Vergalla\",\"Simone Mestici\",\"Giacomo Acciarini\",\"Bala Poduval\",\"Olga Verkhoglyadova\",\"Madhulika Guhathakurta\",\"Thomas E. Berger\",\"Frank Soboczenski\",\"Atılım Güneş Baydin\"]","published":"2025-11-19T00:58:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"astro-ph.EP\"]","methods":"[]","has_code":false}