Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

Abstract

Abstract. Zenith Total Delay (ZTD) time series, derived from the re-processing of Global Positioning System (GPS) data, provide valuable information for the evaluation of global atmospheric reanalysis products such as ERA-Interim. Identifying 10 the correct noise characteristics in the ZTD time series is an important step to assess the 'true' magnitude of ZTD trend uncertainties. The ZTD residual time series for 1995-2015 are generated from our homogeneously re-processed and homogenized GPS time series from over 700 globally distributed stations classified into five major climate zones. The annual peak of ZTD data ranges between 10 and 150 mm with the smallest values for the polar and Alpine zone. The amplitudes of daily curve fall between 0 and 12 mm with the greatest variations for the dry zone. The autoregressive process 15 of fourth order plus white noise model were found to be optimal for ZTD series. The tropical zone has the largest amplitude of autoregressive noise (9.59 mm) and the greatest amplitudes of white noise (13.00 mm). All climate zones have similar median coefficients of AR(1) (0.80±0.05) with a minimum for polar and Alpine, which has the highest coefficients of AR (2) (0.27±0.01) and AR(3) (0.11±0.01) and clearly different from the other zones considered. We show that 53 of 120 examined trends became insignificant, when the optimum noise model was employed, compared to 11 insignificant trends for pure 20 white noise. The uncertainty of the ZTD trends may be underestimated by a factor of 3 to 12 compared to the white noise only assumption.

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

“…The mean SR significantly decreases with the increase of the amplitude of white noise, and the minimum SR value is 74.4%, which is resulted from a 3 mm level of white noise. Table 2 indicates that our selection of noise agrees well with existing studies (Klos et al., 2016), where a noise level of global GNSS‐PWV time series is under 3 mm. Therefore, the SR results mean the AAHT method performs very well on a global scale.…”

“…Previous studies stated that the noise in an atmospheric time series, e.g. the zenith wet delay of GNSS, PWV, and temperature over a site, can be expressed by a combination of the white noise and first-order autoregressive (AR(1)) noise (Alshawaf et al, 2017;Klos et al, 2018;. If the noise is large, it is difficult to correctly identify those true changepoints in the time series, which may lead to an aliasing mechanism (the true changepoints with slight offsets may be hidden by the large observation noise), if any, and also hide slight offsets and lead to more false alarms in the detection results.…”

“…The flowchart for the above procedure is illustrated in Figure 2 below. The performance of the AAHT method is assessed through a Monte Carlo simulation using a simulated dataset based on the characteristics of GNSS-PWV time series from global stations used in previous studies (Jin et al, 2008;Klos et al, 2018;Tong Ning & Elgered, 2012;Zhao et al, 2019). The reason for not using real/observed benchmark time series is to avoid the effect of potential unsubstantiated inhomogeneity in the time series on the detection results.…”

“…Currently, observations from global navigation satellite systems (GNSS) have been used to retrieve PWV time series (GNSS-PWV) due to their high temporal resolution and low cost. Since Bevis (Bevis et al, 1992) proposed GNSS meteorology in the early 1990s, GNSS-PWV time series have been widely used in various climatic studies and applications (Klos et al, 2018;Nilsson & Elgered, 2008;Tong Ning & Elgered, 2012; X. Wang et al, 2018;Zhao et al, 2020).…”

Tropospheric products of the second GOP European GNSS reprocessing (1996–2014)

“…Although the individual station trend provided in the Supplement could be compared to other studies -e.g. Baldysz et al (2016), Klos et al (2016), or Nilsson and Elgered (2008) -it should be strongly emphasized here that our trends are estimated without any preceding time-series homogenization and the formal errors of the trend estimates are underestimated by a factor 2-4 (Nilsson and Elgered, 2008). Statistics GO1-GO0 GO2-GO1 GO3-GO1 GO4-GO1 GO5-GO4 GO6-GO4 Table 6 summarizes the statistics of estimated trend differences at all 172 stations, always between particular variants as defined in Sect.…”