Modeling and prediction of the ionosphere with deep learning: a review

Abstract

The ionosphere plays a crucial role in the transmission and propagation of space signals. As a component of the upper atmosphere, it exhibits distinct spatio-temporal variations and is influenced by solar and geomagnetic activities. Accurately modeling and predicting the ionosphere remains a significant challenge. Recent advancements in deep learning techniques have provided valuable insights into these challenges, offering new approaches for spatio-temporal ionospheric modeling and prediction. By integrating multiple observations from both space-borne and ground-based stations, high-resolution digital models of the ionosphere can be constructed using convolutional and recurrent neural networks. This paper reviews the recent progress in ionospheric modeling and prediction using deep learning networks, discusses the advantages of deep learning models over traditional empirical models, and outlines future directions to address the remaining challenges in this field.

Keywords

Ionosphere modeling / deep learning / empirical ionosphere models / artificial intelligence / GNSS observations

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