IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics

TL;DR

IonCast is a deep learning framework that accurately forecasts global ionospheric Total Electron Content by integrating diverse data sources and leveraging spatiotemporal modeling, enhancing space weather prediction capabilities.

Contribution

The paper introduces IonCast, a novel deep learning model specifically designed for ionospheric forecasting, combining physical drivers with observational data in a scalable graph-based approach.

Findings

IonCast outperforms persistence models in storm-time and quiet conditions.

The model effectively integrates heterogeneous datasets for improved TEC prediction.

Demonstrates potential for operational space weather forecasting.

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.