Estimates of Vertical Velocity Errors for IGS ITRF2014 Stations by Applying the Improved Singular Spectrum Analysis Method and Environmental Loading Models
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A reliable subtraction of seasonal signals from the Global Positioning System (GPS) position time series is beneficial for the accuracy of derived velocities. In this research, we propose a two-stage solution of the problem of a proper determination of seasonal changes. We employ environmental loading models (atmospheric, hydrological and ocean non-tidal) with a dominant annual signal of amplitudes in their superposition of up to 12 mm and study the seasonal signal (annual and semi-annual) estimates that change over time using improved singular spectrum analysis (ISSA). Then, this deterministic model is subtracted from GPS position time series. We studied data from 376 permanent International GNSS Service (IGS) stations, derived as the official contribution to International Terrestrial Reference Frame (ITRF2014) to measure the influence of applying environmental loading models on the estimated vertical velocity. Having removed the environmental loadings directly from the position time series, we noticed the evident change in the power spectrum for frequencies between 4 and 80 cpy. Therefore, we modelled the seasonal signal in environmental models using the ISSA approach and subtracted it from GPS vertical time series to leave the noise character of the time series intact. We estimated the velocity dilution of precision (DP) as a ratio between classical Weighted Least Squares and ISSA approach. For a total number of 298 out of the 376 stations analysed, the DP was lower than 1. This indicates that when the ISSA-derived curve was removed from the GPS data, the error of velocity becomes lower than it was before.
KeywordsGPS seasonal signals singular spectrum analysis environmental loadings ITRF2014 dilution of precision
Anna Klos, Marta Gruszczynska and Janusz Bogusz are financed by the Polish National Science Centre, Grant No. UMO-2014/15/B/ST10/03850. Machiel Simon Bos is financially supported by Portuguese funds through FCT in the scope of the Project IDL-FCT-UID/GEO/50019/2013 and Grant Number SFRH/BPD/89923/2012. Jean-Paul Boy is partly funded by CNES (Centre National d’Etudes Spatiales), through its TOSCA program. Loading time series used here are available at EOST/IPGS loading service (http://loading.u-strasbg.fr). Maps and charts were plotted in the Generic Mapping Tool (Wessel et al. 2013). IGS time series were accessed from ftp://igs-rf.ensg.eu/pub/repro2.
- Bogusz, J., Gruszczynska, M., Klos, A., & Gruszczynski, M. (2015). Non-parametric estimation of seasonal variations in GPS-derived time series. Springer IAG Symposium Series, 146. Springer Berlin Heidelberg, doi: 10.1007/1345_2015_191.
- Collilieux, X., Altamimi, Z., Coulot, D., Ray, J., & Sillard, P. (2007). Comparison of very long baseline interferometry, GPS, and satellite laser ranging height residuals from ITRF2005 using spectral and correlation methods. Journal of Geophysical Research, 112, B12403. doi: 10.1029/2007JB004933.CrossRefGoogle Scholar
- Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553–597. doi: 10.1002/qj.828.CrossRefGoogle Scholar
- Farrell, W. E. (1972). Deformation of the earth by surface loads. Reviews of Geophysics, 10(761–797), 1972.Google Scholar
- Freymueller, J.T. (2009). Seasonal position variations and regional Reference frame realization. In H. Drewes (Ed.), Geodetic reference frames, International Association of Geodesy Symposia 134 (pp. 191–196). Springer Berlin Heidelberg, doi: 10.1007/978-3-642-00860-3_30.
- Kenyeres, A., & Bruyninx, C. (2009). Noise and Periodic Terms in the EPN Time Series. In H. Drewes (Ed.), Geodetic reference frame: International Association of Geodesy Symposia 134 (pp. 143–148). Springer Berlin Heidelberg, doi: 10.1007/978-3-642-00860-3_22.
- Kontny, B., & Bogusz, J. (2012). Models of vertical movements of the Earth crust surface in the area of Poland derived from leveling and GNSS data. Acta Geodynamica et Geomaterialia, 9(3), 331–337.Google Scholar
- Kumar, A., Walia, V., Arore, B. R., Yanh, T. F., Lin, S.-J., Fu, Ch-Ch., et al. (2015). Identifications and removal of diurnal and semidiurnal variations in radon time series data of Hsinhua monitoring station in SW Taiwan using singular spectrum analysis. Natural Hazards, 79(1), 317–330. doi: 10.1007/s11069-015-1844-1.CrossRefGoogle Scholar
- Menemenlis, D., Campin, J., Heimbach, P., Hill, C., Lee, T., Nguyen, A., et al. (2008). ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, 31, 13–21.Google Scholar
- Teferle, F. N., Bingley, R. M., Dodson, A. H., Penna, N. T., & Baker, T. F. (2002). Using GPS to separate crustal movements and sea level changes at tide gauges in the UK. In H. Drewes, A. H. Dodson, L. P. S. Fortes, L. Sanchez, & P. Sandoval (Eds.), Vertical reference systems (pp. 264–269). Heidelberg: Springer.CrossRefGoogle Scholar