scholarly journals Least-Squares Spectral and Coherency Analysis of the Zenith Total Delay Time Series at SuomiNet Station SA56 (UNB2)

2020 ◽  
Author(s):  
Anthony O. Mayaki ◽  
Marcelo Santos ◽  
Thalia Nikolaidou
2016 ◽  
Author(s):  
Anna Klos ◽  
Addisu Hunegnaw ◽  
Felix Norman Teferle ◽  
Kibrom Ebuy Abraha ◽  
Furqan Ahmed ◽  
...  

Abstract. Zenith Total Delay (ZTD) time series, derived from the re-processing of Global Positioning System (GPS) data, provide valuable information for the evaluation of global atmospheric reanalysis products such as ERA-Interim. Identifying the correct noise characteristics in the ZTD time series is an important step to assess the "true" magnitude of ZTD trend uncertainties. The ZTD residual time series for 1995–2015 are generated from our homogeneously re-processed and homogenized GPS time series from over 700 globally distributed stations classified into five major climate zones. The annual peak of ZTD data ranges between 10 and 150 mm with the smallest values for the polar and Alpine zone. The amplitudes of daily curve fall between 0 and 12 mm with the greatest variations for the dry zone. The autoregressive process of fourth order plus white noise model were found to be optimal for ZTD series. The tropical zone has the largest amplitude of autoregressive noise (9.59 mm) and the greatest amplitudes of white noise (13.00 mm). All climate zones have similar median coefficients of AR(1) (0.80 ± 0.05) with a minimum for polar and Alpine, which has the highest coefficients of AR(2) (0.27 ± 0.01) and AR(3) (0.11 ± 0.01) and clearly different from the other zones considered. We show that 53 of 120 examined trends became insignificant, when the optimum noise model was employed, compared to 11 insignificant trends for pure white noise. The uncertainty of the ZTD trends may be underestimated by a factor of 3 to 12 compared to the white noise only assumption.


2016 ◽  
Author(s):  
Anna Klos ◽  
Addisu Hunegnaw ◽  
Felix Norman Teferle ◽  
Kibrom Ebuy Abraha ◽  
Furqan Ahmed ◽  
...  

GPS Solutions ◽  
2021 ◽  
Vol 25 (3) ◽  
Author(s):  
Liangke Huang ◽  
Ge Zhu ◽  
Lilong Liu ◽  
Hua Chen ◽  
Weiping Jiang

2001 ◽  
Vol 7 (1) ◽  
pp. 97-112 ◽  
Author(s):  
Yulia R. Gel ◽  
Vladimir N. Fomin

Usually the coefficients in a stochastic time series model are partially or entirely unknown when the realization of the time series is observed. Sometimes the unknown coefficients can be estimated from the realization with the required accuracy. That will eventually allow optimizing the data handling of the stochastic time series.Here it is shown that the recurrent least-squares (LS) procedure provides strongly consistent estimates for a linear autoregressive (AR) equation of infinite order obtained from a minimal phase regressive (ARMA) equation. The LS identification algorithm is accomplished by the Padé approximation used for the estimation of the unknown ARMA parameters.


2012 ◽  
Vol 4 (2) ◽  
pp. 149-154 ◽  
Author(s):  
Adrian Letchford ◽  
Junbin Gao ◽  
Lihong Zheng

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