of seasonal signals becomes less important, and the powerlaw character of the residuals starts to play a crucial role in the determined velocity uncertainties. With reference frame and sea level applications in mind, we argue that 7 and 9 years of continuous observations is the threshold for white and flicker noise, respectively, while 17 years are required for random-walk to decrease GDP below 5% and to omit periodic oscillations in the GNSS-derived time series taking only the noise model into consideration.
Long series of Zenith Wet Delay (ZWD) obtained as part of a homogeneous re-processing of Global Positioning System solutions constitute a reliable set of data to be assimilated into climate models. The correct stochastic properties, i.e. the noise model of these data, have to be identified to assess the real value of ZWD trend uncertainties since assuming an inappropriate noise model may lead to over-or underestimated error bounds leading to statistically insignificant trends. We present the ZWD time series for 1995-2017 for 120 selected globally distributed stations. The deterministic model in the form of a trend and significant seasonal signals were removed prior to the noise analysis. We examined different stochastic models and compared them to widely assumed white noise (WN). A combination of the autoregressive process of first-order plus WN (AR(1) + WN) was proven to be the preferred stochastic representation of the ZWD time series over the generally assumed white-noise-only approach. We found that for 103 out of 120 considered stations, the AR(1) process contributed to the AR(1) + WN model in more than 50% with noise amplitudes between 9 and 68 mm. As soon as the AR(1) + WN model was employed, 43 trend estimates became statistically insignificant, compared to 5 insignificant trend estimates for a white-noise-only model. We also found that the ZWD trend uncertainty may be underestimated by 5-14 times with median value of 8 using the white-noise-only assumption. Therefore, we recommend that AR(1) + WN model is employed before tropospheric trends are to be determined with the greatest reliability.
In GNSS data processing, the station height, receiver clock and tropospheric delay (ZTD) are highly correlated to each other. Although the zenith hydrostatic delay of the troposphere can be provided with sufficient accuracy, zenith wet delay (ZWD) has to be estimated, which is usually done in a random walk process. Since ZWD temporal variation depends on the water vapor content in the atmosphere, it seems to be reasonable that ZWD constraints in GNSS processing should be geographically and/or time dependent. We propose to take benefit from numerical weather prediction models to define optimum random walk process noise. In the first approach, we used archived VMF1-G data to calculate a grid of yearly and monthly means of the difference of ZWD between two consecutive epochs divided by the root square of the time lapsed, which can be considered as a random walk process noise. Alternatively, we used the Global Forecast System model from National Centres for Environmental Prediction to calculate random walk process noise dynamically in real-time. We performed two representative experimental campaigns with 20 globally distributed International GNSS Service (IGS) stations and compared real-time ZTD estimates with the official ZTD product from the IGS. With both our approaches, we obtained an improvement of up to 10% in accuracy of the ZTD estimates compared to any uniformly fixed random walk process noise applied for all stations.
In Global Navigation Satellite System (GNSS) coordinate time-series unrecognized errors and unmodelled (periodic) effects may bias nonlinear motions induced by geophysical signals.Hence, understanding and mitigating these errors is vital to reducing biases and on revealing subtle geophysical signals. To assess the nature of periodic signals in coordinate time-series Precise Point Positioning (PPP) solutions for the period 2008-2015 are generated. The solutions consider Global Positioning System (GPS), GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) or combined GPS+GLONASS (GNSS) observations. We assess the periodic signals of station coordinates computed using the combined International GNSS Service (IGS) and four of its Analysis Centers (ACs) products. Furthermore, we make use of different filtering methods to investigate the sources of the periodic signals. A faint fortnightly signal in our PPP solution based on Jet Propulsion Laboratory (JPL) products and the existence of an 8 d period for those ACs generating combined GPS+GLONASS products are the main features in the GPS-only solutions. The existence of the 8 d period in the GPS-only solution indicates that GPS orbits computed in a combined GNSS solution contain GLONASS-specific signals. The GLONASS-only solution shows highly elevated powers at the third draconitic harmonic (∼120 d period), at the 8 d period and its harmonics (4 d, 2.67 d) besides the wellknown annual, semi-annual and other draconitic harmonics. We show that the GLONASS constellation gaps before December 2011 contribute to the power at some of the frequencies. However, the well-known fortnightly signal in GPS-only solutions is not discernible in the GLONASS-only solution. The combined GNSS solution contains periodic signals from both systems, with most of the powers being reduced when compared to the single-GNSS solutions. A 52 per cent reduction for the horizontal components and a 36 per cent reduction for the vertical component are achieved for the fortnightly signal from the GNSS solution compared to the GPS-only solution. Comparing the results of the employed filtering methods reveals that the source of most of the powers of draconitic and fortnightly signals are satellite-induced with a non-zero contribution of site-specific errors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.