Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions

Abstract

We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical-Canadian High Arctic Ionospheric Model (E-CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite-years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E-CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid-latitude performance with absolute errors lower than the IRI by 2.5 × 10⁹ to 2.5 × 10¹⁰ e⁻/m³, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 10⁹ e⁻/m³ at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E-CHAIM.