Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data

Wilgan, Karina; Hurter, F.; Geiger, Alain; Rohm, Witold; Bosy, Jarosław

doi:10.1007/s00190-016-0942-5

Cited by 35 publications

(29 citation statements)

References 27 publications

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“…The introduction of these atmospheric products showed a measurable improvement in the accuracy of the forecasts (Yan et al 2009;Mahfouf et al 2015;Wilgan et al 2016). Although the assimilation of ZWD data into climate models is a relatively new problem, a perfect example of the process is the ongoing assimilation of the ZWD time series into a model of the Uncertainty Estimation in Regional ReAnalysis (UERRA) Project managed by the UK Met Office.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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“…For the calm period, the biases are usually the smallest and increase with the variations of the troposphere; thus, for the severe period, the biases are generally the largest. Moreover, for stations that are not surrounded by many other GNSS stations, such as SWKI or ZYWI, the collocation model depends mainly on WRF, which is biased with respect to the GNSS data (Wilgan et al 2017). Thus, those stations exhibit larger biases than stations that are surrounded by many other GNSS stations, such as BYDG or LODZ.…”

Section: High-resolution a Priori Ztdmentioning

confidence: 99%

“…The thorough description of the least-squares collocation technique and the ZTD COMEDIE from Polish data can be found in Wilgan et al (2017). Basically, the collocation estimates the deterministic part, the signal and the noise of each measurement.…”

Section: High-resolution a Priori Ztdmentioning

confidence: 99%

Real-time precise point positioning augmented with high-resolution numerical weather prediction model

et al. 2017

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The tropospheric delay is one of the major error sources in precise point positioning (PPP), affecting the accuracy and precision of estimated coordinates and convergence time, which raises demand for a reliable tropospheric model, suitable to support PPP. In this study, we investigate the impact of three tropospheric models and mapping functions regarding position accuracy and convergence time. We propose a routine to constrain the tropospheric estimates, which we implemented in the in-house developed real-time PPP software. We take advantage of the high spatial resolution (4 9 4 km 2 ) numerical weather prediction Weather Research and Forecasting (WRF) model and near real-time GNSS data combined by the least-squares collocation estimation to reconstruct the tropospheric delays. We also present mapping functions calculated from the WRF model using the ray-tracing technique. The performance tests are conducted on 14 Polish EUREF Permanent Network (EPN) stations during 3 weeks of different tropospheric conditions: calm, standard and severe. We consider six GNSS data processing variants, including two commonly used variants using a priori ZTD and mapping functions from UNB3m and VMF1-FC models, one with a priori ZTD and mapping functions calculated directly from WRF model and three variants using the aforementioned mapping functions but with ZTD model based on GNSS and WRF data used as a priori troposphere and to constrain tropospheric estimates. The application of a high-resolution GNSS/WRF-based ZTD model and mapping functions results in the best agreement with the official EPN coordinates. In both static and kinematic modes, this approach results in an average reduction of 3D bias by 20 and 10 mm, respectively, but an increase of 3D SDs by 1.5 and 4 mm, respectively. The application of high-resolution tropospheric model also shortens the convergence time, for example, for a 10 cm convergence level, from 67 to 58 min for the horizontal components and from 79 to 63 min for the vertical component.

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“…Studies on estimation of GNSSderived ZTD in Africa have been enabled due to the presence of the GPS ground receiver sites for geodetic studies for some regions such as those from African Monsoon Multidisciplinary Analysis (AMMA) project, 1999-2005 over West Africa Bock and Nuret, 2009), the realization of the African Geodetic Reference Frame (AFREF), and South Africa's national network (TRIGNET) among others. These have initiated the investigation of ZTD or PWV over some of these regions such as (Wilgan et al, 2017;Bock et al, 2005Bock et al, , 2007Koulali et al, 2011). The East African tropical region has also recently benefited from the Africa Array (AA) which has created a multidisciplinary research network for the broader Earth science community, by installing continuous Global Positioning System (CGPS) instruments fitted with meteorological (met) sensors (http://www.…”

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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Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data

Cited by 35 publications

References 27 publications

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

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

Real-time precise point positioning augmented with high-resolution numerical weather prediction model

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

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