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arXiv Machine Learning By Mert Can Turkmen, Eng Leong Tan, Yee Hui Lee

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

Read the original on arXiv Machine Learning →

The paper introduces IonoDGNN, a dynamic graph neural network that models the ionosphere over ionospheric pierce points, conditioning forecasts on future satellite trajectories (ephemeris conditioning). It predicts ionospheric irregularities, measured by the Rate of TEC Index, up to two hours ahead using GNSS data from Singapore, achieving a Brier Skill Score of 0.55 and a PR-AUC of 0.77—significant improvements over persistence and interpolation baselines. The model remains robust under simulated coverage dropouts, leveraging spatial message passing to maintain predictive skill on affected nodes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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