IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics
This work addresses forecasting challenges for ionospheric variability, relevant to space weather resilience and operational applications like aviation and communications, but it appears incremental as it builds on existing GraphCast-inspired methods.
The paper tackles the problem of forecasting ionospheric dynamics, which affects GNSS accuracy and communications, by introducing IonCast, a deep learning framework that improves forecasting skill compared to persistence methods.
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.