1. Seasonal Position Variations and Regional Reference Frame Realization Jeff Freymueller Geophysical Institute University of Alaska Fairbanks

2. Motivation

3. Questions What is the cause of these observed seasonal variations? –Snow and ice loading? –Systematic errors in orbits, tidal loading, etc.? Do other sites (used to realize the ITRF) include seasonal variations not included in the ITRF model? –In standard time series, these sites have smaller seasonals than sites not used to realize the frame. Does the error go into the frame? –Does the method of frame realization affect seasonal signals at local/regional sites of interest?

4. Regional realization vs. ITRF ITRF solutions uses –Minimum constraints –“Free” velocity solution –Velocity and frame constraints added –Seasonal variation affects frame only if it biases mean velocities Regional solution or time series alignment –Daily or weekly realization of ITRF using frame sites –Positions may not be linear in time, cannot assume linear velocity of all sites in solution –Seasonal variation in frame sites may impact frame realization, and thus regional time series

5. Outline A non-parametric method for assessing seasonal variations What are seasonal variations at frame realization sites? –Apply an iterative approach Impact of seasonal variations on regional solution Conclusions

6. Non-parametric Seasonal

7. Method 1.Remove linear or long-term trend 2.Stack residuals by fractional year (e.g. Jan 1-10), to get mean residual for that time period 1.40 seasonal bins (9.125 days each) 2.5 point smoothing applied to bin averages 3.Daily seasonal variation derived by linear interpolation

8. EastHeight 0.51.01.5 0.51.01.5 1997200719972007

9. Regional Network

10. Solution Details Daily network solutions using fixed orbit, free network GIPSY software GOA4 –Ocean tidal loading –Relative phase center models –10 degree elevation –Time dependent ZTD + constant azimuthal tropospheric variation –ITRF2000/IGS03

11. Estimate Seasonals 18 sites were used to realize ITRF Estimate seasonal components for each site –Realize frame using the 17 other sites –Estimate seasonal variation –Repeat for all sites –Then iterate on solution, applying seasonal corrections estimated in the prior iteration. –Results presented after 5 iterations

12. Examples (scale +/- 2 cm) FLIN YELL ALGO GOLD

13. Classification Large seasonal variations ( > 5 mm) –BRMU, GOLD, IRKT, NYAL, THU1/3, TROM, TRO1, YELL Moderate seasonal variations (~4-5 mm) –ALGO, CHUR, MDO1, NLIB, PENT Small seasonal variation (< 4 mm) –DUBO, FLIN, STJO, TIXI Seasonals at TROM, TRO1 different in horizontal (similar in vertical)

14. Time Series – IRKT (Irkutsk) Original – Linear ITRFAfter final iteration

15. Impact On User Sites Generate 1 year synthetic time series –Seasonal variations from final iteration at all reference frame realization sites –NO seasonal variations at test sites –Realize ITRF each day assuming no seasonal variation at frame sites Test sites show seasonal variations –1-2 mm amplitude horizontal –2-4 mm amplitude horizontal –These variations are purely the result of a seasonal bias in the frame realization

16. Error Induced in Time Series Fairbanks, AlaskaWestern Aleutians

17. Conclusions Seasonal variations at many sites exceed 5 mm/yr amplitude – 2 ppb peak to peak –At most northern sites, pattern is consistent with snow and ice loading Seasonal variations bias daily or weekly frame realization –Frame parameters and station coordinates –Biases become embedded in time series Be careful in deciding what is the “data” A challenge for assessing time-dependent deformation Seasonals should be assessed at a global scale, perhaps with ITRF2005 residuals

18. Vertical WRMS