Articles | Volume 44, issue 1
https://doi.org/10.5194/angeo-44-85-2026
https://doi.org/10.5194/angeo-44-85-2026
Regular paper
 | 
03 Feb 2026
Regular paper |  | 03 Feb 2026

Plasma density estimation from ionograms and geophysical parameters with deep learning

Kian Sartipzadeh, Andreas Kvammen, Björn Gustavsson, Njål Gulbrandsen, Magnar G. Johnsen, Devin Huyghebaert, and Juha Vierinen

Data sets

Replication Data for: Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning Kian Sartipzadeh et al. https://doi.org/10.18710/CFSVA2

Model code and software

Replication Data for: Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning Kian Sartipzadeh et al. https://doi.org/10.18710/CFSVA2

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Short summary
Knowledge of the charged environment in the upper atmosphere is essential for understanding space weather effects on satellites and radio communication. This environment is difficult to estimate at high latitudes, where aurora cause strong variability. We developed an artificial intelligence model to estimate this environment continuously. Our results show that the model provides reliable estimates even during auroral activity, improving monitoring of the polar upper atmosphere.
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