Seasonal variability of GPS‐derived zenith tropospheric delay (1994–2006) and climate implications

Abstract

[1] The total zenith tropospheric delay (ZTD) is an important parameter of the atmosphere and directly or indirectly reflects the weather and climate processes and variations. In this paper the ZTD time series with a 2-hour resolution are derived from globally distributed 150 International GPS Service (IGS) stations (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006), which are used to investigate the secular trend and seasonal variation of ZTD as well as its implications in climate. The mean secular ZTD variation trend is about 1.5 ± 0.001 mm/yr at all IGS stations. The secular variations are systematically increasing in most parts of the Northern Hemisphere and decreasing in most parts of the Southern Hemisphere. Furthermore, the ZTD trends are almost symmetrically decreasing with increasing altitude, while the summation of upward and downward trends at globally distributed GPS sites is almost zero, possibly reflecting that the secular ZTD variation is in balance at a global scale. Significant annual variations of ZTD are found over all GPS stations with the amplitude from 25 to 75 mm. The annual variation amplitudes of ZTD near oceanic coasts are generally larger than in the continental inland. Larger amplitudes of annual ZTD variation are mostly found at middle latitudes (near 20°S and 40°N) and smaller amplitudes of annual ZTD variation are located at higher latitudes (e.g., Antarctic) and the equator areas. The phase of annual ZTD variation is about 60°in the Southern Hemisphere (about February, summer) and about 240°in the Northern Hemisphere (about August, summer). The mean amplitude of semiannual ZTD variations is about 10 mm, much smaller than annual variations. The semiannual amplitudes are larger in the Northern Hemisphere than in the Southern Hemisphere, indicating that the semiannual variation amplitudes of ZTD in the Southern Hemisphere are not significant. In addition, the higher-frequency variability (RMS of ZTD residuals) ranges from 15 to 65 mm of delay, depending on altitude of the station. Inland stations tend to have lower variability and sites at ocean and coasts have higher variability. These seasonal ZTD cycles are due mainly to the wet component variations (ZWD).

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

“…The maximum of the semi-annual cycle falls in January for the Northern Hemisphere and are uncorrelated in phase for the 35 Southern Hemisphere. Similar results were reported before by Jin et al (2007). Atmos.…”

“…The main variability of ZTD comes from ZWD, as was shown in this study in a pre-analysis and also stated earlier by Jin et al (2007). Temporal variations determined here showed a good agreement in amplitude and phase for annual curves for both Northern and Southern Hemisphere.…”

“…In this case we adopt ordinary least-squares (LS) and estimate the covariance of fitted parameters as in equation (5). This approach is widely used in ZTD/IWV estimations in terms of climate applications (e.g., Jin et al, 2007;Flouzat et al, 2009;Morland et al, 2009;Ning et al, 2013;Bock et al, 2014). Nilsson and Elgered (2008) applied additional covariance as a function of time so as not to assume a pure white noise process.…”

“…Typically, the tropical zone is characterized by high day-to-day anomalies of GPS derived ZTD time series (e.g., Jin et al, 2008). Oceanic 30 coasts are thought to have greater annual variations than any other regions (e.g., Jin et al, 2007), while regions between 20° and 65° for both Hemispheres (N and S) are characterized by large values of PWV linear trends of 4 kg/m 2 per decade determined for PWV from the European Reanalysis Interim (ERAI) model and DORIS data (Bock et al, 2014). This might be a reason to suggest that the ZTD residuals ε ZTD , or their stochastic model from equation (2) behave in different ways for each climate zone examined.…”

Rain pattern analysis and forecast model based on GPS estimated atmospheric water vapor content

“…We used three 147 12-hour slicing window in each day to get hourly PW values, removing the first and the 148 last values from each slicing window to avoid the edge effect of the Gauss-Markov 149 process (Jin et al, 2007). The computation strategy was the usual in this kind of 150 calculations, and it is shown in table 2 (Champollion et al, 2004;Cucurull et al, 2004;151 Brenot et al, 2006;Jin et al, 2007). 152 …”