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New neural network predicts ionospheric irregularities up to 9 hours ahead

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel data-driven, multiscale method to predict ionospheric irregularities using a neural network architecture inspired by graph neural networks. This approach leverages data from Super Dual Auroral Radar Network (SuperDARN) and SECIRA radar systems, combined with space weather parameters like geomagnetic and solar disturbance indices, and satellite-measured solar wind and interplanetary magnetic field data. The model demonstrates acceptable forecast quality with a lead time of up to 7-9 hours, with the highest accuracy achieved for nowcasting. AI

IMPACT This research could improve space weather forecasting by enabling more accurate predictions of ionospheric irregularities.

RANK_REASON This is a research paper detailing a new method for predicting ionospheric irregularities using neural networks and radar data. [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 neural network predicts ionospheric irregularities up to 9 hours ahead

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This is a research paper detailing a new method for predicting ionospheric irregularities using neural networks and radar data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Oleg I. Berngardt, Ivan A. Lavygin ·

    Space weather dependence of decameter-scale ionospheric irregularities spatial distribution

    arXiv:2610.29630v1 Announce Type: cross Abstract: This paper describes an approach to constructing nowcast and forecast predictive models for the occurrence of high-latitude decameter ionospheric irregularities based on SuperDARN and SECIRA radar data and space weather parameters…

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