Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning

2026-04-20

AI Summary

This work addresses the limitations of existing ionospheric irregularity prediction models, which rely on gridded data and fail to preserve the temporal sampling structure of satellite observations, thereby hindering accurate forecasting of newly emerging GNSS links within future fields of view. To overcome this, the authors propose IonoDGNN—a dynamic graph neural network conditioned on satellite ephemerides—that models the ionosphere as a time-evolving graph aligned with satellite trajectories. By leveraging predictable ephemeris data to preconstruct future graph topologies and employing spatial message passing for rolling forecasts, IonoDGNN enables, for the first time, predictions for rising satellites not yet observed. Evaluated on dual-receiver data from Singapore (2023–2025) for 5-minute-resolution ROTI forecasting up to two hours ahead, the model achieves a Brier Skill Score of 0.49 and a PR-AUC of 0.75, representing 35% and 52% improvements over a persistence baseline, respectively, while raising the AUC for rising satellites from 0.52 to 0.95.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Most data-driven ionospheric forecasting models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-based sensing. We instead model the ionosphere as a dynamic graph over ionospheric pierce points (IPPs), with connectivity that evolves as satellite positions change. Because satellite trajectories are predictable, the graph topology over the forecast horizon can be constructed in advance. We exploit this property to condition forecasts on the future graph structure, which we term ephemeris conditioning. This enables prediction on lines of sight that appear only in the forecast horizon. We evaluate our framework on multi-GNSS (Global Navigation Satellite System) data from a co-located receiver pair in Singapore spanning January 2023 through April 2025. The task is to forecast Rate of TEC Index (ROTI)-defined irregularities at 5-minute cadence up to 2 hours ahead as binary probabilistic classification per node. The resulting model, IonoDGNN, achieves a Brier Skill Score (BSS) of 0.49 and a precision-recall area under the curve (PR-AUC) of 0.75, improving over persistence by 35\% in BSS and 52\% in PR-AUC, with larger gains at longer lead times. Ablations confirm that graph structure and ephemeris conditioning each contribute meaningfully, with conditioning proving essential for satellites that rise during the forecast horizon (receiver operating characteristic AUC: 0.95 vs.\ 0.52 without). Under simulated coverage dropout, the model retains predictive skill on affected nodes through spatial message passing from observed neighbors. These results suggest that dynamic graph forecasting on evolving lines of sight is a viable alternative to grid-based representations for ionospheric irregularity forecasting. The model and evaluation code will be released upon publication.

Problem

• ionospheric irregularities
• GNSS
• forecasting
• dynamic graph
• lines of sight

Innovation

• dynamic graph neural network
• ephemeris conditioning
• ionospheric irregularities
• GNSS
• pierce point forecasting