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A Neural network model of Electron density in Earth's Topside ionosphere (NET)

Authors
/persons/resource/asmirnov

Smirnov,  Artem
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;
2.7 Space Physics and Space Weather, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/yshprits

SHPRITS,  YURI
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;
2.7 Space Physics and Space Weather, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Prol,  Fabricio
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

/persons/resource/hluehr

Lühr,  H.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;
2.3 Geomagnetism, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/irina

Zhelavskaya,  Irina
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;
2.7 Space Physics and Space Weather, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Berrendorf,  Max
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Xiong,  Chao
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Smirnov, A., SHPRITS, Y., Prol, F., Lühr, H., Zhelavskaya, I., Berrendorf, M., Xiong, C. (2023): A Neural network model of Electron density in Earth's Topside ionosphere (NET), XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4800


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021206
Abstract
The ionosphere is an ionized part of the upper atmosphere, where the number of free electrons is large enough to affect the propagation of electromagnetic signals, including those of the Global Navigation Satellite Systems (GNSS) systems. Knowing electron density values in the ionosphere is crucial for both industrial and scientific applications. Here, we present a novel Neural network model of Electron density in Earth's Topside ionosphere (NET). The model is trained on 19 years of radio occultation data collected by the CHAMP, GRACE, and COSMIC missions. We assume a linear decay of scale height with altitude and create a model consisting of 4 parameters, namely the F2-peak density and height (NmF2 and hmF2) and the slope and intercept of scale height decay in the topside (dHs/dh and H0). The resulting NET model, based on feedforward neural networks, takes as input the geographic and magnetic position, magnetic local time, day of year, and solar and geomagnetic indices. The model has been extensively validated on more than a hundred million in-situ measurements from CHAMP, CNOFS and Swarm satellites, as well as on the GRACE/KBR data. The NET model yields highly accurate and unbiased reconstructions of electron density in the topside ionosphere for all seasonal and solar activity conditions and can have wide applications in ionospheric research.