Long series of Zenith Wet Delay (ZWD) obtained as part of a homogeneous re-processing of Global Positioning System solutions constitute a reliable set of data to be assimilated into climate models. The correct stochastic properties, i.e. the noise model of these data, have to be identified to assess the real value of ZWD trend uncertainties since assuming an inappropriate noise model may lead to over-or underestimated error bounds leading to statistically insignificant trends. We present the ZWD time series for 1995-2017 for 120 selected globally distributed stations. The deterministic model in the form of a trend and significant seasonal signals were removed prior to the noise analysis. We examined different stochastic models and compared them to widely assumed white noise (WN). A combination of the autoregressive process of first-order plus WN (AR(1) + WN) was proven to be the preferred stochastic representation of the ZWD time series over the generally assumed white-noise-only approach. We found that for 103 out of 120 considered stations, the AR(1) process contributed to the AR(1) + WN model in more than 50% with noise amplitudes between 9 and 68 mm. As soon as the AR(1) + WN model was employed, 43 trend estimates became statistically insignificant, compared to 5 insignificant trend estimates for a white-noise-only model. We also found that the ZWD trend uncertainty may be underestimated by 5-14 times with median value of 8 using the white-noise-only assumption. Therefore, we recommend that AR(1) + WN model is employed before tropospheric trends are to be determined with the greatest reliability.

In Global Navigation Satellite System (GNSS) coordinate time-series unrecognized errors and unmodelled (periodic) effects may bias nonlinear motions induced by geophysical signals.Hence, understanding and mitigating these errors is vital to reducing biases and on revealing subtle geophysical signals. To assess the nature of periodic signals in coordinate time-series Precise Point Positioning (PPP) solutions for the period 2008-2015 are generated. The solutions consider Global Positioning System (GPS), GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) or combined GPS+GLONASS (GNSS) observations. We assess the periodic signals of station coordinates computed using the combined International GNSS Service (IGS) and four of its Analysis Centers (ACs) products. Furthermore, we make use of different filtering methods to investigate the sources of the periodic signals. A faint fortnightly signal in our PPP solution based on Jet Propulsion Laboratory (JPL) products and the existence of an 8 d period for those ACs generating combined GPS+GLONASS products are the main features in the GPS-only solutions. The existence of the 8 d period in the GPS-only solution indicates that GPS orbits computed in a combined GNSS solution contain GLONASS-specific signals. The GLONASS-only solution shows highly elevated powers at the third draconitic harmonic (∼120 d period), at the 8 d period and its harmonics (4 d, 2.67 d) besides the wellknown annual, semi-annual and other draconitic harmonics. We show that the GLONASS constellation gaps before December 2011 contribute to the power at some of the frequencies. However, the well-known fortnightly signal in GPS-only solutions is not discernible in the GLONASS-only solution. The combined GNSS solution contains periodic signals from both systems, with most of the powers being reduced when compared to the single-GNSS solutions. A 52 per cent reduction for the horizontal components and a 36 per cent reduction for the vertical component are achieved for the fortnightly signal from the GNSS solution compared to the GPS-only solution. Comparing the results of the employed filtering methods reveals that the source of most of the powers of draconitic and fortnightly signals are satellite-induced with a non-zero contribution of site-specific errors.

We characterize the spatial-temporal variability of integrated water vapor (IWV) in Ethiopia from a network of global positioning system (GPS) stations and the European Center for Medium range Weather Forecasting (ECMWF) model. The IWV computed from the ECMWF model is integrated from the height of the GPS stations on 60 pressure levels to take both the actual earth surface and the model orography discrepancies into account. First, we compare the IWV estimated from GPS and from the model. The bias varies from site to site, and the correlation coefficients between the two datasets exceed 0.85 at different time scales. The results of this study show that the general ECMWF IWV trend is underestimation over highlands and overestimation over lowlands for wet periods, and overestimation over highlands and underestimation over lowlands for dry periods with very few exceptional stations. Second, we observe the spatial variation of the IWV. High values are obtained in those stations that are located in the north-eastern (Afar depression) sites and the south-western part of the country. This distribution is related to the spatial variability of the climate in Ethiopia. Finally, we study the seasonal cycle and inter-annual variability of IWV for all stations over Ethiopia. The main result is the strong inter-annual variability observed for the dry seasons.