  Homogenization is an important and crucial step to improve the usage of
observational data for climate analysis. This work is motivated by the analysis
of long series of GNSS Integrated Water Vapour (IWV) data which have not yet
been used in this context. This paper proposes a novel segmentation method that
integrates a periodic bias and a heterogeneous, monthly varying, variance. The
method consists in estimating first the variance using a robust estimator and
then estimating the segmentation and periodic bias iteratively. This strategy
allows for the use of the dynamic programming algorithm that remains the most
efficient exact algorithm to estimate the change-point positions. The
statistical performance of the method is assessed through numerical
experiments. An application to a real data set of 120 global GNSS stations is
presented. The method is implemented in the R package GNSSseg that will be
available on the CRAN.
