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New dynamic graph model forecasts ionospheric irregularities with high accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach to forecasting ionospheric irregularities using dynamic graphs and ephemeris conditioning. This method models the ionosphere as a graph where connectivity changes with satellite positions, allowing predictions on lines of sight that may only appear in the future. Tested on Global Navigation Satellite System data from Singapore, the IonoDGNN model achieved a Brier Skill Score of 0.55 and an AUC of 0.77 for predicting Rate of TEC Index irregularities up to two hours in advance. The study indicates that dynamic graph forecasting offers a viable alternative to traditional gridded ionospheric models, especially when dealing with coverage dropouts. AI

IMPACT Introduces a novel graph-based AI approach for improving ionospheric forecasting, potentially enhancing GNSS accuracy.

RANK_REASON Academic paper detailing a new methodology and model for a scientific forecasting task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dynamic graph model forecasts ionospheric irregularities with high accuracy

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Academic paper detailing a new methodology and model for a scientific forecasting task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mert Can Turkmen, Eng Leong Tan, Yee Hui Lee ·

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

    arXiv:2604.18379v3 Announce Type: replace Abstract: Most data-driven ionospheric 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 po…

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