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

TL;DR

This paper introduces IonoDGNN, a dynamic graph-based model that forecasts ionospheric irregularities along satellite lines of sight by leveraging ephemeris-conditioned graph structures, outperforming traditional grid-based methods.

Contribution

The paper presents a novel dynamic graph framework with ephemeris conditioning for ionospheric forecasting, enabling predictions on lines of sight that appear only in the forecast horizon.

Findings

IonoDGNN achieves a Brier Skill Score of 0.49 and PR-AUC of 0.75, outperforming persistence.

Ephemeris conditioning significantly improves forecast accuracy, especially for satellites rising during the horizon.

The model maintains predictive skill under coverage dropout through spatial message passing.

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.