Accurate Reconstructions of the Earth's Ionosphere Using Neural Networks
Category Computer Science Tuesday - May 2 2023, 15:36 UTC - 1 year ago Neural networks are used to improve our ability to reconstruct the Earth's ionosphere for satellite navigation and other applications. Researchers from the GFZ German Research Centre for Geosciences have introduced a new model which is based on neural networks and 19 years of satellite measurement data. This model can reconstruct the topside ionosphere much more precisely than before, which has important implications in ionospheric research.
Neural networks have significantly improved our ability to reconstruct the Earth’s atmospheric layer, making it possible to achieve much more accurate results. This has significant implications for satellite navigation, among other applications.
The ionosphere, a region of geospace located between 60 and 1000 kilometers above Earth, disrupts radio signal transmission from global navigation satellite systems (GNSS) due to its electrically charged particles. This interference poses challenges for the increasing precision demanded by these systems in both research and practical applications, such as autonomous driving or accurate satellite orbit determination.
To compensate for ionospheric delays, which are a major source of error in GNSS applications, models of the ionosphere and its fluctuating, dynamic charge distribution can be utilized. A new ionospheric model has been introduced by researchers Artem Smirnov and Yuri Shprits from the GFZ German Research Centre for Geosciences. This model, which is based on neural networks and 19 years of satellite measurement data, was published in the journal Scientific Reports.
In particular, it can reconstruct the topside ionosphere, the upper, electron-rich part of the ionosphere much more precisely than before. It is thus also an important basis for progress in ionospheric research, with applications in studies on the propagation of electromagnetic waves or for the analysis of certain space weather events, for example.
The Earth’s ionosphere is the region of the upper atmosphere that extends from about 60 to 1000 kilometers in altitude. Here, charged particles such as electrons and positive ions dominate, caused by the radiation activity of the Sun – hence the name. The ionosphere is important for many scientific and industrial applications because the charged particles influence the propagation of electromagnetic waves such as radio signals.
The so-called ionospheric propagation delay of radio signals is one of the most important sources of interference for satellite navigation. This is proportional to the electron density in the space traversed. Therefore, a good knowledge of the electron density can help in correcting the signals. In particular, the upper region of the ionosphere, above 600 kilometers, is of interest, since 80 percent of the electrons are gathered in this so-called topside ionosphere.
The problem is that the electron density varies greatly – depending on the longitude and latitude above the Earth, the time of day and year, and solar activity. This makes it difficult to reconstruct and predict them, the basis for correcting radio signals, for example.
There are various approaches to modeling electron density in the ionosphere, among others, the International Reference Ionosphere Model IRI, which has been recognized since 2014. It is an empirical model that establishes a relationship between input and output variables based on the statistical analysis of observations. However, it still has weaknesses in the important area of the topside ionosphere because of the limite observation data available.
An alternative approach has been proposed in a paper by Artem Smirnov and Yuri Shprits from the GFZ German Research Centre for Geosciences. The two authors are researching the physics of the outer space environment and the development of methods for its mapping.
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