A New Adaptive Absolute Method for Homogenizing GNSS-Derived Precipitable Water Vapor Time Series

The inhomogeneities, the so-called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS-PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal-Trend decomposition based on LOESS (STL) and Penalized Maximal F-test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS-PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth-generation European center for medium-range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long-term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA-PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization.