Statistical significance of trends in Zenith Wet Delay from re-processed GPS solutions

Kłos, Anna; Hunegnaw, Addisu; Teferle, Felix Norman; Abraha, Kibrom Ebuy; Ahmed, Furqan; Bogusz, Janusz

doi:10.1007/s10291-018-0717-y

Cited by 33 publications

(26 citation statements)

References 40 publications

(41 reference statements)

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“…This increment was also presented by Weatherhead et al (). Using hourly data, σ b when using the autoregressive model is 5 to 14 times higher than the case of using the simple white noise model as presented by Klos et al (). These results confirm the necessity of using the autoregressive model instead of a white noise process when assessing the significance of the estimated climatic trends.…”

Section: Resultsmentioning

confidence: 59%

On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series

et al. 2018

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For more than two decades, Global Positioning System (GPS) tropospheric delays have successfully been exploited to monitor the tropospheric water vapor in near real time and reprocessing mode. Although reprocessed data are considered reliable for climatic research, it is important to address the often present gaps, inhomogeneities, and to use a proper model to describe the stochastic part of the time series so that trustworthy trends are estimated. Having relatively long time series, daily reprocessed tropospheric Zenith Total Delay, precipitable water vapor (PWV), and gradients from the Tide Gauge benchmark monitoring network are used in this work to estimate climatic trends. We use a first-order autoregressive model AR(1) to describe the residuals after the trend estimation so that a correct trend uncertainty is estimated. Using the same model, we obtain the number of years of PWV data required to estimate statistically significant trends. For comparison, we produce tropospheric parameters at each Tide Gauge station based on ERA-Interim refractivity fields. We found that 83% of 64 GPS stations show a positive PWV trend below 1 mm/decade independent of the time interval, with approximately half of the trends indicated significant. There is a strong correlation (86%) between the Global Navigation Satellite Systems and ERA-Interim PWV trends. The trends tend to increase when moving east and south on the European map. The results show a percentage change of PWV of 3-8% per a degree Celsius rise in temperature. The number of years required to detect significant PWV trends varies between 30 and 40 years.

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Section: Resultsmentioning

confidence: 59%

On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series

et al. 2018

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“…The stochastic part due to noise and errors is included in ε t and is modeled as the sum of an autoregressive noise of the first order AR(1) and a white noise (WN). This model is inspired from the results of Klos et al (2018), who analyzed the stochastic properties of GPS ZWD (zenith wet delay) data. Parameters a , b , c i , s i , and o j are estimated in one single run using maximum likelihood estimation (MLE) in the Hector software (Bos et al, 2013).…”

Section: Datamentioning

confidence: 99%

“…Klos et al (2018) found that a combination of AR(1) plus white noise (WN) provides a good stochastic representation of the ZWD time series of GPS stations. We adopt here this model for the IWV difference data set:

ε_{t} = v_{t} + w_{t},

where v t is an AR(1) process and w t is a WN process.…”

Section: Datamentioning

confidence: 99%

Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets

Malderen

Pottiaux

Kłos

et al. 2020

Earth and Space Science

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We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA-Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this "complex experiment" is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations.

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“…The ZHD can be adequately modeled from surface pressure or using pressure data from models (Numerical weather models or alternatively, empirical meteorological models). The ZWD is either directly reported or computed as unknown parameter by subtracting ZHD from ZTD (Klos et al, 2018). At GNSS frequencies, tropospheric delay can be over 2 m at the zenith and over 20 m at the lower elevation angles (Kouba, 2009;Yao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Variability and accuracy of Zenith Total Delay over the East African tropical region

Ssenyunzi

Oruru

D’ujanga

et al. 2019

Advances in Space Research

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The Global Navigation Satellite System (GNSS) can be used to derive accurately the Zenith Tropospheric Delay (ZTD) under allweather conditions. The derived ZTDs play a vital role in climate studies, weather forecasting and are operationally assimilated into numerical weather prediction models. In this study, variations and statistical analysis of GNSS-derived ZTD over the East African tropical region are analysed. The data is collected from 13 geodetic permanent stations for the period of 4 years from 2013 to 2016. The 13 stations consist of 5 International GNSS Service (IGS) stations plus 8 stations as follows: 4 Africa Array stations and 4 Malawi Rifting stations from Uganda, Kenya, Tanzania and Rwanda. The ZTD time series were processed using goGPS software version 1.0 beta1, a MATLAB based GNSS processing software, originally developed for kinematic applications but recently re-engineered for quasi static applications. The annual variation of the ZTD time series was investigated using Lomb Scargle periodograms. The semi-annual frequency has the dominant power in subregion 1 (latitudes 4 S and 4 N) and the annual frequency has the dominant power in subregion 2 (latitudes 12 S to 4 S). The highest ZTD estimates occur during the rainy seasons, at all stations, and the lowest estimates occur during the dry seasons. The results also show that the ZTD estimates are largest at stations located at low elevation (regions close to the Indian Ocean). The derived ZTDs are compared to the values derived from the GIPSY-OASIS via Jet Propulsion Laboratory (JPL) online Automatic Precise Positioning Service (APPS) and the Unified Environmental Modelling System (UEMS) numerical weather prediction (NWP) model. The comparison of goGPS and APPS ZTD at the 13 stations shows an overall average bias, Root Mean Square (RMS) and standard deviation (stdev) of À0.9 mm, 3.2 mm and 3.0 mm respectively, with correlation coefficients ranging from 0.974 to 0.999. The comparison of goGPS ZTD against UEMS NWP ZTD at 8 selected stations shows average bias, RMS and stdev of À12.4 mm, 22.0 mm and 17.6 mm respectively, with correlation coefficients ranging from 0.802 to 0.974. The agreement between the GPS ZTD and the NWP ZTD indicates that goGPS ZTD can be assimilated into NWP models in the East African region.

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Statistical significance of trends in Zenith Wet Delay from re-processed GPS solutions

Cited by 33 publications

References 40 publications

On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series

On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series

Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets

Variability and accuracy of Zenith Total Delay over the East African tropical region

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