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 betwee
The ionosphere is a layer of the Earth’s upper atmosphere that is ionized by solar radiation and plays a crucial role in communication and navigation.
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 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.
Recently, however, large amounts of data have become available for this area. Therefore, Machine learning approaches lend themselves to deriving regularities from this, especially for complex non-linear relationships.A team from the GFZ German Research Centre for Geosciences around Artem Smirnov, Ph.D. student and first author of the study, and Yuri Shprits, head of the “Space Physics and Space Weather” section and Professor at University Potsdam, took a new ML-based empirical approach.
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