Development and Implications of the Singapore GBAS Ionospheric Threat Model (GITM)

2017 
Anomalous ionospheric behavior in low-latitude regions is known to threaten the integrity of Ground-Based Augmentation Systems (GBAS) users due to the potential for large gradients in ionospheric delays between GBAS ground stations and approaching aircraft. This report describes the development of a GBAS ionospheric threat model (GITM) for Singapore Changi Airport based on an analysis of local ionospheric anomalies observed by two networks of ground stations in Singapore. The threat model can then be included in simulations of GBAS precision approaches to determine the impact to integrity and performance. Dual-frequency Global Navigation Satellite System (GNSS) data collected over 35 months from 2011 through early 2017, with emphasis on the spring and autumn equinoxes, were analyzed to estimate the parameters of this threat model. All of the anomalous gradients detected above 150 mm/km appear to have been produced by equatorial plasma bubbles, which are regions of depleted ionospheric delay that occur at times during post-sunset and nighttime hours in low-latitude regions. This report presents the analysis methods used to compute and characterize the spatial ionospheric gradient magnitude; propagation speed, direction of motion, and width of the plasma bubble; and maximum slant delay depletion. About 180 anomalous gradient observations comprising about 50 separate plasma bubble events were observed and analyzed. The maximum slant ionospheric gradient observed was 526 mm/km on GPS satellite PRN 32 on 26 March 2012. The results for gradient, velocity, tilt angle, width, and depth were roughly similar to those obtained in other low-latitude studies, except that a pair of very small plasma bubbles with a primary width (per bubble) of about 3 km was observed on PRN 7 on 9 April 2016, with a maximum gradient just below 200 mm/km. A separate component of the GITM was included to bound these very narrow bubbles with a lower maximum gradient.
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