Earth Topside Ionosphere Electron Density Prediction for the Advancement of Space Weather Forecasting

Abstract:

Modeling the Earth’s ionosphere is a critical component of forecasting space weather. Since many physics-based models often make simplifying assumptions which decouple the interactions between layers of the atmosphere/ionosphere, they are not as robust to sudden disturbances from high solar activity. In contrast, machine learning techniques allow for model creation with fewer assumptions, since the coupling to other portions of the space weather system is empirically defined. This research focuses specifically on predicting the electron density in the topside of the ionosphere, using data collected 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 with two hidden layers was developed and trained on two solar cycles worth of data, including features such as time series F10.7, time series average interplanetary magnetic field (IMF), time series Kp, solar azimuth, and location to generate an electron density prediction. We tested the model on six years of subsequent data, and found a correlation coefficient of 0.7 for a nowcast of electron density. The included figure depicts the predicted electron densities along with the true densities measured by a DMSP satellite over a 5-hour period. These early results are promising, and greatly outperform climatological modeling techniques. Future work will improve testing performance via modified model inputs and typical machine learning techniques. A forecast will then be computed by providing the nowcast model with forecasted inputs; these inputs are more easily measured and forecasted compared to the electron density of the ionosphere. Therefore, we eventually hope to better forecast the electron density of the Earth’s ionosphere and in turn better predict space weather, mitigating its negative effects.