Neural network model of Electron density in the Topside ionosphere (NET)

Abstract

The ionosphere is an ionized part of the upper atmosphere, where the number of electrons in is large enough to affect the propagation of electromagnetic signals, including those of the GNSS systems. Therefore, knowing electron density values in the ionosphere is crucial for both industrial and scientific applications. Here, we employ the radio occultation profiles collected by the CHAMP, GRACE, and COSMIC missions, to model the electron density in the topside ionosphere. We assume a linear decay of scale height with altitude and create a model of 4 parameters, namely the F2-peak density and height (NmF2 and hmF2) and the slope and gradient of scale height in the topside (H0 and dHs/dh). The resulting model (NET) is based on feedforward neural networks and takes as input the geographic and geomagnetic position, the solar flux and geomagnetic indices. The resulting density reconstructions are validated on more than a hundred million in-situ measurements from CHAMP, CNOFS and Swarm satellites, as well as on the GRACE/KBR data, and the developed model is compared to several topside options of the Internation Reference Ionosphere (IRI) model. The NET model yields highly accurate reconstructions of electron density in the topside ionosphere and gives unbiased predictions for all seasonal and solar activity conditions.