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.
Cite
@article{arxiv.2511.15004,
title = {IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics},
author = {Halil S. Kelebek and Linnea M. Wolniewicz and Michael D. Vergalla and Simone Mestici and Giacomo Acciarini and Bala Poduval and Olga Verkhoglyadova and Madhulika Guhathakurta and Thomas E. Berger and Frank Soboczenski and Atılım Güneş Baydin},
journal= {arXiv preprint arXiv:2511.15004},
year = {2025}
}
Comments
11 pages, 7 figures, 3 tables. Accepted as a poster presentation at the Machine Learning for the Physical Sciences Workshop at NeurIPS 2025