Asymmetric positioning errors in GNSS time-series: a study from different world regions

SUMMARY Global Navigation Satellite System (GNSS) plays a fundamental role in monitoring time-dependent ground displacement. However, GNSS daily position time-series can often contain significant outliers, reaching up to several centimetres. These are likely of non-tectonic origin, and, if not properly accounted for, they can significantly impact the accuracy and dependability of the estimation of key parameters for geophysical analyses, such as long-term velocities and transient deformations. Characterizing these outliers can provide information about their possible sources and help us implement mitigation strategies. Asymmetric outliers, that is, those characterized by a primary direction, therefore occurring on one side of the mean time-series, are of particular interest since they could point to the presence of recurring or repeatable sources of error. Their key features and potential causes are, however, still not fully analysed and understood.We analyse asymmetric outliers in thousands of GNSS time-series across three regions—Central-Southern Italy, New Zealand and the Western U.S.—using data from multiple processing centres, and we reveal some persistent features among all data sets. Tens of the analysed sites exhibit hundreds of large outliers (10–50 mm), far exceeding typical position uncertainties ($\sim$1–6 mm). Remarkably, the outliers are numerous in the horizontal component, and tend to occur near mountainous regions, with preferred direction roughly orthogonal to the local topography. The results consistency across different data sets and instrumental features suggest a physical origin for these outliers rather than a specific processing approach or instrumental configuration. Further analyses at local scales align with previous studies linking skewed position errors to uncorrected tropospheric delays driven by the coupling between atmospheric conditions and local terrain (e.g. trapped lee waves). However, other factors—such as multipath, snow accumulation on GNSS antennas or obstructed sky visibility—could also contribute to the observed asymmetric outliers. We explore mitigation strategies at both processing and post-processing stages, but further analyses and more sophisticated approaches, such as high-resolution tropospheric modelling, are needed to better understand the involved processes and achieve meaningful improvements.