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Estimating climatic trends using GNSS

During the recent two decades ground-based GNSS has developed to a powerful and widely acknowledged atmospheric remote sensing method. The GNSS data have been increasingly used to estimate atmospheric precipitable water vapor (PWV) from observations of global and regional GNSS ground networks. The related PWV time is now adequately long (~20 years) so that the data can be exploited for climate change related studies. We use the time series of PWV and temperature to estimate climatic trends using regional and global data sets from GNSS, ERA-Interim, and in situ data.

The research region in Germany is well covered by 351 permanent GNSS sites with an average separation distance of 30 km. Homogeneous time series with length of 10 to 19 years are available from 119 sites. The second data set we used is the ERA-Interim reanalysis with a spatial resolution of 79 km in longitude and latitude, 60 vertical levels with the model top at 0.1 hPa (about 64 km), and 6 hours temporal resolution. Besides, there are 326 meteorological stations operated by the German Meteorological Service (DWD) with data profiles of more than 60 years at a temporal rate of one hour. The estimation of the trend requires the assessment of the homogeneity of the time series, which is done in a preprocessing step.

The results show mainly positive trends, which agree with the trends estimated from the corresponding ERA-Interim time series, as shown in Figure 1. More details about the trends and possible spatial patterns were obtained by analyzing 26 years of ERA-Interim data, which show positive trends over Germany that increase in the north-east direction.

This work is currently extended to the TIGA network that contains 840 stations globally distributed with time series since 1995.


[1] Ning, T., Wickert, J., Deng, Z., Heise, S., Dick, G., Vey, S., Schöne, T. : Homogenized time series of the atmospheric water vapor content obtained from the GNSS reprocessed data. Journal of Climate, 29, 7, p. 2443-2456, doi: 0.1175/JCLI-D-15-0158.1, 2016.

[2] Ning, T., Wang, J., Elgered, G., Dick, G., Wickert, J., Bradke, M., Sommer, M.: The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations. Atmospheric Measurement Techniques, 9, p. 79-92, doi: 10.5194/amt-9-79-2016, 2016.

[3] Alshawaf, F., Balidakis, K., Dick, G., Heise, S., and Wickert, J.: Estimating trends in atmospheric water vapor and temperature time series over Germany. Atmos. Meas. Tech., 10, 3117-3132, doi.org/10.5194/amt-10-3117-2017, 2017.

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