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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.
