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2006 AGU Fall Meeting. 14 Dec. 2006, San Francisco – Poster #G43A-0985 Jim Ray (NOAA/NGS), Tonie van Dam (U. Luxembourg), Zuheir Altamimi (IGN), Xavier.

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Presentation on theme: "2006 AGU Fall Meeting. 14 Dec. 2006, San Francisco – Poster #G43A-0985 Jim Ray (NOAA/NGS), Tonie van Dam (U. Luxembourg), Zuheir Altamimi (IGN), Xavier."— Presentation transcript:

1 2006 AGU Fall Meeting. 14 Dec. 2006, San Francisco – Poster #G43A-0985 Jim Ray (NOAA/NGS), Tonie van Dam (U. Luxembourg), Zuheir Altamimi (IGN), Xavier Collilieux (IGN) Abstract. Prior studies of the power spectra of GPS position time series have found pervasive seasonal signals against a power-law background of white noise plus flicker noise [e.g., S. Williams et al., JGR, 109, B03412, 2004]. D. Dong et al. [JGR, 107(B4), 2002] estimated that less than half the observed GPS seasonal power can be explained by redistributions of geophysical fluid mass loads. Much of the residual variation is probably caused by unidentified GPS technique errors and analysis artifacts. Among possible mechanisms, N. Penna and M. Stewart [GRL, 30(23), 2003] have shown how unmodeled analysis errors at tidal frequencies (near 12- and 24-hour periods) can be aliased to longer periods very efficiently. Signals near fortnightly, semi-annual, and annual periods are expected to be most seriously affected. We have examined spectra of the 167 sites of the International GNSS Service (IGS) network having at least 200 weekly measurements during The non-linear residuals of the weekly IGS solutions that were included in ITRF2005 have been used. To improve the detection of common-mode signals, the normalized spectra of all sites have been stacked, then smoothed with a boxcar filter (0.03 cpy window), for each local N, E, and H component. Anomalous Harmonics in the Spectra of GPS Position Estimates 1. Spectra of Raw GPS Time Series – Stacked & Smoothed The stacked, smoothed spectra are similar for all three components. Peaks are evident at harmonics of about 1 cpy up to at least 6 cpy, but the peaks are not at strictly 1.0 cpy intervals. Based on the 6th tones of the spectra, which are among the sharpest peaks, and assuming a linear overtone model, then a common fundamental of ± cpy can explain all peaks well. A flicker noise power-law continuum describes the background spectrum down to periods of a few months, after which the residuals become whiter. Similar sub-seasonal tones are not apparent in the residuals of available SLR and VLBI sites, which are both 10 times less numerous and dominated by white noise. There is weak evidence for a few isolated 1 cpy overtones in the spectra of geophysical loadings, but these are much noisier than for GPS positions. Alternatively, as pointed out by U. Hugentobler (TU Munich), the period of the cpy frequency, about days, is very close to a "GPS year"; i.e., the interval required for the constellation to repeat its inertial orientation with respect to the sun. This could indicate that the harmonics are a type of systematic error related to the orbits. Mechanisms could involve modeling defects or aliasing of site-dependent positioning biases. 2. Filtered GPS Spectra – Remove Seasonal Fits3. Compare to SLR Spectra since signals at 1.0 & 2.0 cpy are likely due to geophysical & technique sources, fit & remove those first then recompute stacked, smooth spectra as before harmonics at >2 cpy are still apparent assume linear overtone model for harmonics average of narrow 6 th N,E,H peaks gives fundamental tone of ± cpy model fits observed peaks well, except seasonal peaks affected by pre-filtering (see red dashed lines) flicker noise describes background spectra down to periods of a few months at shorter intervals, residuals become whiter stacked spectra (Lomb periodogram) for 167 IGS sites with >200 weekly points during using non-linear residuals from the ITRF2005 combination smoothed using boxcar filter with 0.03 cpy window spectra are very similar for all 3 components harmonics seen near 1 cpy up to at least ~6 cpy frequencies for harmonics >2 cpy are definitely not at even 1.0 cpy intervals compare observed spectral peaks with 1.0 intervals (marked with red dashed lines) contributions also probable at 1.0 & 2.0 cpy due to geophysical signals & technique errors Raw SLR Spectra use ITRF2005 non-linear residuals for 18 sites with >200 weekly points ~1 cpy peaks seen in all 3 components no sub-seasonal harmonics seen spectra are dominated by white noise insufficient sensitivity to detect harmonic signals if they are present (e.g., geophysical) Raw GPS Spectra Filtered GPS Spectra

2 2006 AGU Fall Meeting. 14 Dec. 2006, San Francisco – Poster #G43A Conclusions 4. Compare to VLBI Spectra 5. Compare to Filtered Atmosphere Pressure Loading – Remove Seasonal Fits 6. Compare to Filtered Surface Water Loading – Remove Seasonal Fits 8. GPS “Draconitic” Year use ITRF2005 non-linear residuals for 21 sites with >200 daily sessions only clear peak is ~1 cpy in H residuals no sub-seasonal harmonics seen spectra are dominated by white noise insufficient sensitivity to detect harmonic signals if they are present (e.g., geophysical) Raw VLBI Spectra remove large seasonal peaks before computing stacked, smoothed spectra for same 167 IGS sites hydrology model apparently possesses N * 1.0 harmonics but no sub-seasonal harmonics seen at GPS frequencies the cpy frequency of the GPS harmonics does not match any expected alias also does not match any geophysical frequency however, U. Hugentobler [private communication, 2006] suggested a possible explanation he pointed out that the GPS harmonic has the same period as the GPS “draconitic” year ± cpy corresponds to period of ± 1.7 days a GPS year is the time (viewed from the Earth) for the Sun to return to the same point in space relative to the GPS orbital nodes GPS nodes drift in space primarily due to effect of the Earth’s oblateness, about º/year so “GPS year” = days, corresponding to a frequency of cpy possible mechanisms could involve constraints of GPS orbit model parameterization also possible that unknown site position biases are modulated by GPS year due to varying geometry remove large seasonal peaks before computing stacked, smoothed spectra for same 167 IGS sites no clear evidence for sub-seasonal harmonics, except maybe near 3.1 cpy 7. Compare to Filtered Non-tidal Ocean Loading – Remove Seasonal Fits remove large seasonal peaks before computing stacked, smoothed spectra for same 167 IGS sites no clear evidence for sub-seasonal harmonics, except maybe near 6.2 cpy Filtered Atmosphere Loading Spectra Filtered Ocean Loading Spectra Filtered Hydrologic Loading Spectra when stacked, spectra of GPS position time series show sub-seasonal harmonics, up to at least ~6 cpy observations are consistent with overtones of a fundamental tone of ± cpy (or ± 1.7 day period) in addition, usual seasonal signals (1.0 & 2.0 cpy) are prominent spectra are similar for all 3 N,E,H components flicker noise describes background spectra down to periods of a few months position residuals become whiter at shorter periods comparisons with SLR & VLBI are inconclusive because of small sample of sites & higher noise annual variations seen in all 3 SLR components & in VLBI heights, but no harmonics check of geophysical fluid loadings fails to reveal source for sub-seasonal harmonics rather than a geophysical cause, GPS technique errors are suspected as underlying source of non-seasonal harmonics if so, mechanism appears to be related to position of GPS constellation could involve GPS orbit modeling or possible site position biases are modulated by GPS year variation (i.e., apparent position of constellation relative to sites) such geometrical biases have been demonstrated due to antenna near-field reflections beating of 1.0 & cpy tones should generate beat modulations at & cpy – or periods of 25.6 years & days long-period beat modulation could bias velocity estimates if samples are not collected often enough fits for seasonal geophysical signals will be contaminated by cpy harmonic unless geophysical effect is much larger further research is required to investigate direct links between these effects 10. Consequences


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