IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics

The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. Traditional physics-based models are computationally demanding and limited in assimilating real-time heterogeneous data. We present IonCast, a deep learning model adapted from GraphCast and tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global total electron content (TEC), integrating diverse heliophysical 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. I also like the former sentence: IonCast contributes a scientifically grounded, extensible platform for advancing heliophysics and operational space weather resilience.