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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.15004 |
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| _version_ | 1866911275954470912 |
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| author | Kelebek, Halil S. Wolniewicz, Linnea M. Vergalla, Michael D. Mestici, Simone Acciarini, Giacomo Poduval, Bala Verkhoglyadova, Olga Guhathakurta, Madhulika Berger, Thomas E. Soboczenski, Frank Baydin, Atılım Güneş |
| author_facet | Kelebek, Halil S. Wolniewicz, Linnea M. Vergalla, Michael D. Mestici, Simone Acciarini, Giacomo Poduval, Bala Verkhoglyadova, Olga Guhathakurta, Madhulika Berger, Thomas E. Soboczenski, Frank Baydin, Atılım Güneş |
| contents | The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired model tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global Total Electron Content (TEC), integrating diverse physical drivers and observational datasets. Validating on held-out storm-time and quiet conditions highlights improved skill compared to persistence. By unifying heterogeneous data with scalable graph-based spatiotemporal learning, IonCast demonstrates how machine learning can augment physical understanding of ionospheric variability and advance operational space weather resilience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics Kelebek, Halil S. Wolniewicz, Linnea M. Vergalla, Michael D. Mestici, Simone Acciarini, Giacomo Poduval, Bala Verkhoglyadova, Olga Guhathakurta, Madhulika Berger, Thomas E. Soboczenski, Frank Baydin, Atılım Güneş Machine Learning Earth and Planetary Astrophysics The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired model tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global Total Electron Content (TEC), integrating diverse physical drivers and observational datasets. Validating on held-out storm-time and quiet conditions highlights improved skill compared to persistence. By unifying heterogeneous data with scalable graph-based spatiotemporal learning, IonCast demonstrates how machine learning can augment physical understanding of ionospheric variability and advance operational space weather resilience. |
| title | IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics |
| topic | Machine Learning Earth and Planetary Astrophysics |
| url | https://arxiv.org/abs/2511.15004 |