Improving Electron Density Predictions in the Topside of the Ionosphere Using Machine Learning on In Situ Satellite Data

Abstract

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, 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 (113 satellite-years), along with global drivers and indices such as F10.7, interplanetary magnetic field, and Kp to generate an electron density prediction. We tested the model on six years of subsequent data (26 satellite-years), and found a correlation coefficient of 0.87. Once trained, the model can predict topside electron density at any location specified by latitude and longitude given current/recent geomagnetic conditions. We validated the model via comparison with data from the DEMETER satellite which orbited at a similar altitude, and taking that as an independent source of true electron density values. Comparing our results to the International Reference Ionosphere, we find that our model works better at low to mid-latitudes, and for quiet and moderately disturbed geomagnetic conditions, but not for highly disturbed conditions.