Abstract
Seasonal signatures observed within the Global Navigation Satellite System (GNSS) position time series are routinely modelled as annual and semi-annual periods with constant amplitudes over time. However, in this chapter, we demonstrate that these amplitudes can vary significantly over time, by as much as 3 mm at some stations. Different methods have been developed to estimate the time-varying curves. The advantages and disadvantages of those methods are presented for synthetic data, which mimic the real position time series, including their time-changeability and noise properties. For these series, we conclude that the Kalman filter and an adaptation of the Wiener Filter give the best results. As the Earth’s lithosphere is seasonally loaded and unloaded, we also account for the non-tidal atmospheric, oceanic and continental hydrology loading effects, which contribute the most to the seasonal signatures. We demonstrate that a direct removal of loading effects leads to the significant change in the power of the GPS position time series, especially for frequencies between 8 and 80 cpy; if the noise model is not adapted to this new situation, this causes an underestimation of velocity uncertainty. Therefore, we recommend to use the Kalman filter or adaptive Wiener filter methods instead to remove the seasonal signal to ensure accurate estimates of the trend error.
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Acknowledgements
We would like to thank the IGS service for providing the ITRF2014 position time series at http://itrf.ensg.ign.fr/ITRF_solutions/2014/, and the EOST service for providing the environmental loading models at http://loading.u-strasbg.fr/.
This research is financed by the National Science Centre, Poland. The grant was received within the SONATA-12 call, no. UMO-2016/23/D/ST10/00495. Anna Klos is supported by the Foundation for Polish Science (FNP). Machiel S. Bos was sponsored by national Portuguese funds through FCT in the scope of the Project IDL-FCT-UID/GEO/50019/2019 and Grant Number SFRH/BPD/89923/2012.
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Klos, A., Bogusz, J., Bos, M.S., Gruszczynska, M. (2020). Modelling the GNSS Time Series: Different Approaches to Extract Seasonal Signals. In: Montillet, JP., Bos, M. (eds) Geodetic Time Series Analysis in Earth Sciences. Springer Geophysics. Springer, Cham. https://doi.org/10.1007/978-3-030-21718-1_7
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