Ionospheric Electron Density Modeling using Machine Learning

Modeling the Earth's ionosphere is a critical component of forecasting space weather, which in turn impacts radio wave propagation, navigation and communication. This research focuses on predicting the electron density in the topside of the ionosphere using satellite data, in particular from the Defense Meteorological Satellite Program (DMSP), a collection of 19 satellites that have been polar orbiting the Earth for various lengths of times, fully covering 1982 to the present. An artificial neural network was developed and trained on two solar cycles worth of data from DMSP (113 satellite-years), along with global drivers and indices such as F10.7, interplanetary magnetic field (IMF), and Kp to generate an electron density prediction. Here, we present the latest iteration of this model and its performance on out-of-sample DMSP data and DEMETER satellite data, as well as comparison of our model to the International Reference Ionosphere (IRI).