Data-processing induced GPS-positioning error

Global Positioning System data processing is affected by many non-tectonic factors, including the common-mode errors （CME） in station-position time series. The characteristics and origins of CME are still not clear, due to uneven distribution of global GPS networks and the lack of reliable data of the position time series. In this work, data from 241 continuous GPS stations were reprocessed in a consistent way and the results were compared with those generated at Jet Propulsion Laboratory （JPL）. Improvements of residual positions were obtained for many low-quality stations, especially those located in Asia and Australia.

Nontectonic Signals in Reprocessed Continuous GPS Position Time Series of CMONOC

The nearly nine-year continuous GPS data collected since 1 March 1999 from the Crustal Motion Observation Network of China(CMONOC) were consistently analyzed.Most of the nonlinear movements in the cumulative position time series pro-duced by CMONOC data center disappeared;and more accurate vertical terms and tectonic signals were extracted.Displacements caused by atmospheric pressure loading,nontidal ocean loading,soil moisture mass loading,and snow cover mass loading using the National Centers for Environmental Prediction(NCEP) Reanalysis I/II models and Estimation of the Circulation and Climate of the Ocean(ECCO) data can explain most of the vertical annual terms at many stations,while only parts can be explained at Lhasa and southern coastal sites,indicating that there are some deformation mechanisms that are still unknown or not modeled accurately.The remarkable differences in vertical position time series for short-baseline sites reveal that GPS stations can be greatly affected by lo-cal factors;and attention should be paid when explaining observed GPS velocity vectors.

Effect of the span of Australian GPS coordinate time series in establishing an optimal noise model

The span of coordinate time series affects the determination of an optimal noise model. We analyzed position data recorded for 10 continuous Global Positioning System （GPS） sites from 1998.0 to mid-2009 on the Australian Plate to estimate the best noise model and thereafter obtain the true uncertainties of the velocity, employing the maximum likelihood estimation （MLE） method. MLE was employed to analyze the data in four ways. In the first two analyses, the noise was assumed to be a combination of flicker noise and white noise for the raw time series and spatially filtered time series. In the final two analyses, the spectral indices and amplitudes were simultaneously estimated for a power law noise plus white noise model for the raw time series and spatially filtered time series. We conclude that the noise model of GPS time series in Australia can be best described as the combination of flicker noise and white noise. Velocity uncertainties fall below -0.2 mm/yr when the time span exceeds -9.5 years. A comparison of noise amplitudes and maximum likelihood estimation values between the raw and spatially filtered time series suggests that traditional spatial filtering to remove common-mode errors might not be applicable to the raw time series of this region.