Abstract. We use statistical analyses of synthetic position time series to estimate the
potential precision of GPS (Global Positioning System) velocities. The
synthetic series represent the standard range of noise, seasonal, and
position offset characteristics, leaving aside extreme values. This analysis
is combined with a new simple method for automatic offset detection that
allows an automatic treatment of the massive dataset. Colored noise and the
presence of offsets are the primary contributor to velocity variability.
However, regression tree analyses show that the main factors controlling the
velocity precision are first the duration of the series, second the presence
of offsets, and third the noise level (dispersion and spectral index). Our
analysis allows us to propose guidelines, which can be applied to actual GPS
data, that constrain velocity precisions, characterized as a 95 %
confidence limit of the velocity biases, based on simple parameters:
(1) series durations over 8.0 years result in low-velocity biases in the
horizontal (0.2 mm yr−1) and vertical (0.5 mm yr−1) components;
(2) series durations of less than 4.5 years are not suitable for studies that
require precisions lower than mm yr−1; (3) series of intermediate
durations (4.5–8.0 years) are associated with an intermediate horizontal
bias (0.6 mm yr−1) and a high vertical one (1.3 mm yr−1),
unless they comprise no offset. Our results suggest that very long series
durations (over 15–20 years) do not ensure a significantly lower bias
compared to series of 8–10 years, due to the noise amplitude following a
power-law dependency on the frequency. Thus, better characterizations of
long-period GPS noise and pluri-annual environmental loads are critical to
further improve GPS velocity precisions.
Error analysis of continuous GPS position time series
