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arXiv · 2511.15004

IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics

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
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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.

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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. 2025-11-19. IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics. https://arxiv.org/abs/2511.15004

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