Extremely large ionospheric gradients can pose a potential integrity threat to the users of Local Area Augmentation System (LAAS), and thus the development of an ionospheric anomaly threat model is essential for system design and operation. This paper presents a methodology for long-term ionosphere monitoring which will be used to build an ionosphere threat model, evaluate its validity over the life cycle of system, continuously monitor ionospheric anomalies, and update the threat model when necessary. The procedure automatically processes data collected from external sources and networks and estimates ionospheric gradients at regular intervals. If extremely large gradients hazardous to LAAS users are identified, manual validation is triggered. This paper also investigates a simplified truth processing method to create precise ionospheric delay estimates in near real-time, which is the core of long-term ionosphere monitoring. The performance of the method is examined using data from the 20 November 2003 storm and the 31 October 2003 storm. It demonstrates the effectiveness of simplified truth processing within long-term ionosphere monitoring. From the case studies, the automated procedure successfully identified the two worst ionospheric gradients observed and validated to date.
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