The velocity estimates and their uncertainties derived from position time series of Global Navigation Satellite System stations are affected by seasonal signals and their harmonics, and the statistical properties, i.e., the stochastic noise, contained in the series. If the deterministic model in the form of linear trend and periodic terms is not accurate enough to describe the time series, it will alter the stochastic model, and the resulting effect on the velocity uncertainties can be perceived as a result of a misfit of the deterministic model. The effects of insufficiently modeled seasonal signals will propagate into the stochastic model and falsify the results of the noise analysis, in addition to velocity estimates and their uncertainties. We provide the general dilution of precision (GDP) of velocity uncertainties as the ratio of uncertainties of velocities determined from to two different deterministic models while accounting for stochastic noise at the same time. In this newly defined GDP, the first deterministic model includes a linear trend, while the second one includes a linear trend and seasonal signals. These two are tested with the assumption of white noise only as well as the combinations of power-law and white noise in the data. The more seasonal terms are added to the series, the more biased the velocity uncertainties become. With increasing time span of observations, the assumption of seasonal signals becomes less important, and the power-law character of the residuals starts to play a crucial role in the determined velocity uncertainties. With reference frame and sea level applications in mind, we argue that 7 and 9 years of continuous observations is the threshold for white and flicker noise, respectively, while 17 years are required for random-walk to decrease GDP below 5% and to omit periodic oscillations in the GNSS-derived time series taking only the noise model into consideration.

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 well-known 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 have reconstructed integrated water vapor (IWV) using the zenith wet delays to track the properties of hurricanes and explore their spatial and temporal distributions estimated from 922 GPS stations. Our results show that a surge in GPS-derived IWV occurred at least six hours prior to the landfall of two major hurricanes (Harvey and Irma) that struck the Gulf and East Coasts of the USA in 2017. We observed enhanced IWV, in particular, for the two hurricanes landfall locations. The observed variations exhibit a correlation with the precipitation value constructed from GPM/IMERG satellite mission coinciding with hurricane storm front passage. We used GPS-IWV data as inputs for spaghetti line plots for our path predictions, helping us predict the paths of Hurricanes Harvey and Irma. Hence, a directly estimable zenith wet delay sourced from GPS that has not been previously reported can serve as an additional resource for improving the monitoring of hurricane paths.