A more precise model of the Earth's ionosphere

 The ionosphere—the region of geospace spanning from 60 to 1000 kilometers above the Earth—impairs the propagation of radio signals from global navigation satellite systems (GNSS) with its electrically charged particles. This is a problem for the ever higher precision required by these systems—both in research and for applications such as autonomous driving or precise orbit New approach using machine learning and neural networks          

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.

For this, they used data from satellite missions from 19 years, in particular CHAMP, GRACE and GRACE-FO, which were and are significantly co-operated by the GFZ, and COSMIC. The satellites measured—among other things—the electron density in different height ranges of the ionosphere and cover different annual and local times as well as solar cycles.

With the help of Neural Networks, the researchers then developed a model for the electron density of the topside ionosphere, which they call the NET model. They used the so-called MLP method (Multi-Layer Perceptrons), which iteratively learns the network weights to reproduce the data distributions with very high accuracy.

The researchers tested the model with independent measurements from three other satellite missions.

Evaluation of the new model

"Our model is in remarkable agreement with the measurements: It can reconstruct the electron density very well in all height ranges of the topside ionosphere, all around the Globe, at all times of the year and day, and at different levels of solar activity, and it significantly exceeds the International Reference Ionosphere Model IRI in accuracy. Moreover, it covers space continuously," first author Artem Smirnov sums up.

Yuri Shprits adds, "This study represents a paradigm shift in ionospheric research because it shows that ionospheric densities can be reconstructed with very high accuracy. The NET model reproduces the effects of numerous physical processes that govern the dynamics of the topside ionosphere and can have broad applications in ionospheric research."

Possible applications in ionosphere research

The researchers see possible applications, for instance, in wave propagation studies, for calibrating new electron density data sets with often unknown baseline offsets, for tomographic reconstructions in the form of a background model, as well as to analyze specific space weather events and perform long-term ionospheric reconstructions. Furthermore, the developed model can be connected to plasmaspheric altitudes and thus can become a novel topside option for the IRI.

The developed framework allows the seamless incorporation of new data and new data sources. The retraining of the model can be done on a standard PC and can be performed on a regular basis. Overall, the NET model represents a significant improvement over traditional methods and highlights the potential of neural network-based models to provide a more accurate representation of the ionosphere for communication and navigation systems that rely on GNSS.

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