Long-term ionospheric anomaly monitoring for ground based augmentation systems
Article first published online: 25 JUL 2012
©2012. American Geophysical Union. All Rights Reserved.
Volume 47, Issue 4, August 2012
How to Cite
2012), Long-term ionospheric anomaly monitoring for ground based augmentation systems, Radio Sci., 47, RS4006, doi:10.1029/2012RS005016., and (
- Issue published online: 25 JUL 2012
- Article first published online: 25 JUL 2012
- Manuscript Accepted: 6 JUN 2012
- Manuscript Revised: 28 MAY 2012
- Manuscript Received: 18 MAR 2012
- ionospheric anomaly
 Extreme ionospheric anomalies can pose a potential integrity threat to ground-based augmentation of the Global Positioning System (GPS), and thus the development of ionospheric anomaly threat models for each region of operation is essential for system design and operation. This paper presents a methodology for automated long-term ionospheric anomaly monitoring, which will be used to build an ionospheric anomaly threat model, evaluate its validity over the life cycle of the system, continuously monitor ionospheric anomalies, and update the threat model if necessary. This procedure automatically processes GPS data collected from external networks and estimates ionospheric gradients at regular intervals. If ionospheric gradients large enough to be potentially hazardous to users are identified, manual data examination is triggered. This paper also develops a simplified truth processing method to create precise ionospheric delay estimates in near real-time, which is the key to automating the ionospheric monitoring procedure. The performance of the method is examined using data from the 20 November 2003 and 9 November 2004 ionospheric storms. These results demonstrate the effectiveness of simplified truth processing within long-term ionosphere monitoring. From the case studies, the automated procedure successfully identified extreme ionospheric anomalies, including the two worst ionospheric gradients observed and validated previously based on manual analysis. The automation of data processing enables us to analyze ionospheric data continuously going forward and to more accurately categorize ionospheric behavior under both nominal and anomalous conditions.