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Homogenization is an important and crucial step to improve the usage ofobservational data for climate analysis. This work is motivated by the analysisof long series of GNSS Integrated Water Vapour (IWV) data which have not yetbeen used in this context. This paper proposes a novel segmentation method thatintegrates a periodic bias and a heterogeneous, monthly varying, variance. Themethod consists in estimating first the variance using a robust estimator andthen estimating the segmentation and periodic bias iteratively. This strategyallows for the use of the dynamic programming algorithm that remains the mostefficient exact algorithm to estimate the change-point positions. Thestatistical performance of the method is assessed through numericalexperiments. An application to a real data set of 120 global GNSS stations ispresented. The method is implemented in the R package GNSSseg that will beavailable on the CRAN.

A new segmentation method for the homogenisation of GNSS-derived IWV time-series