Ionospheric Modeling by Using a Self-Organizing Map Under Disturbed Conditions

Abstract As solar activity approaches the peak of the 25th solar cycle, the Earth’s upper atmospheric layer, known as the ionosphere, is strongly disturbed by solar activity. Ionospheric disturbances are the main error source of global navigation satellite systems. We propose a real-time ionospheric modeling system (RIMS) based on a self-organizing map (SOM) to accurately model ionospheric conditions. A SOM is an unsupervised machine-learning tool with origins in biological algorithms, having a topological self-organizing structure. The RIMS can model the ionosphere while automatically detecting and removing outliers. Results demonstrate that models generated by the RIMS exhibit high accuracy in quiet and disturbed ionospheric conditions compared with a current actual-use method. The median residual slant total electron content improved from 0.254 to 0.118 total electron content units as compared with the assumed actual-use method. Moreover, the RIMS flexibly and stably models the ionosphere in real time under disturbed conditions without large quantities of data from prior years.