Characteristics of daily position time series from the Hong Kong GPS fiducial network

Characteristics of daily position time series from January, 2001 to August, 2007 at 12 stations in the Hong Kong GPS fiducial network are investigated in this paper. A spatial filtering algorithm based on principal component analysis is employed to remove the common mode errors from the daily position time series. The noise characteristics of the filtered position time series are assessed by the method of maximum likelihood estimation. Contributions from atmospheric, nontidal oceanic, snow and soil moisture mass loading are evaluated. The results indicate that spatial filtering is an effective way to improve the precision of position time series and provide better resolution for detecting local deformation signals. The common mode errors have strong seasonal variation. The observed ∼3 mm annual vertical variation of the common mode errors can be explained by the joint contribution of these seasonal surface mass redistributions. After removing these surface mass loading effects the residual common mode errors are highly related to the higher-order ionospheric effects. The noise in the filtered position time series can be described as a combination of variable white noise plus flicker noise. The velocity uncertainties are about 2 ∼6 times larger if only variable white noise is assumed. The maximum relative horizontal velocity between the sites is 1.5 mm/yr, which indicates some local fault activities. In addition, there are obvious 1-2 mm local seasonal signals in the filtered position time series of some sites. The residual scatters of all filtered time series also show strong seasonal characteristics.