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Extensive ionospheric studies were conducted to support the initial phase of system design approval for the existing SLS‐4000 GBAS installed at Antonio Carlos Jobim International Airport (formerly Galeão International Airport) (GIG) in Rio de Janeiro, Brazil. This paper focuses on determining the broadcast value of the standard deviation of vertical ionospheric gradients (or σvig) that is required to bound ionospheric spatial gradients in Brazil under nominal conditions during daytime hours. The time‐step method is useful for gaining sufficient samples at distances less than the physical separation distance of ground stations and was utilized to estimate ionospheric spatial gradients. A new method called "geometric similarity" was developed to estimate ionospheric temporal gradients and evaluate the temporal effect added to the bounding σvig values. As a result, a σvig of 13 mm/km, including a temporal gradient contribution of approximately 2 mm/km, is conservative enough to bound ionospheric spatial decorrelation for daytime GBAS operations in Brazil. [ABSTRACT FROM AUTHOR]

Prior to initiating GBAS service in equatorial regions, it is vital to evaluate potential integrity threats posed by equatorial plasma bubble (EPB)‐induced ionospheric gradients and assess availability when implementing ionospheric threat mitigation methods. Earlier work developed a preliminary EPB model with a gradient bound larger than twice that for mid‐latitude ionospheric storms. Position‐domain geometry screening (PDGS) with this higher gradient bound decreases availability to 58.3% at the Galeão International Airport, Brazil, during nighttime. A new mitigation method using Monte Carlo simulation randomizes ionospheric scenarios using randomly generated parameter combinations within the threat model and assesses the ensemble impacts. By taking credit for a prior probability of an extreme EPB, this algorithm determines the inflated integrity parameters to meet the safety requirement in the probabilistic definition. This paper shows that with this method, the system availability for category I precision approaches dramatically improved to 89.6% when a data‐driven prior probability of 10‐5 was applied. [ABSTRACT FROM AUTHOR]