Improved Troposphere Blind Models Based on Numerical Weather Data

Möller, Gregor; Weber, Robert L.; Boehm, J.

doi:10.1002/navi.66

Cited by 16 publications

(13 citation statements)

References 8 publications

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“…An extended validation of GPT2w with IGS data and ray-traced delays as well as a comparison with state-of-theart tropospheric models like RTCA-MOPS or the ESA tropospheric blind model can be found in Möller et al (2014). The bias and RMS statistics in that research were, however, computed from the globally merged sample of zenith total delay differences instead of determining them as mean values over all stations as above.…”

Section: Validation With Zenith Total Delays From Gnssmentioning

confidence: 99%

Development of an improved empirical model for slant delays in the troposphere (GPT2w)

et al. 2014

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Global pressure and temperature 2 wet (GPT2w) is an empirical troposphere delay model providing the mean values plus annual and semiannual amplitudes of pressure, temperature and its lapse rate, water vapor pressure and its decrease factor, weighted mean temperature, as well as hydrostatic and wet mapping function coefficients of the Vienna mapping function 1. All climatological parameters have been derived consistently from monthly mean pressure level data of ERA-Interim fields (European Centre for Medium-Range Weather Forecasts Re-Analysis) with a horizontal resolution of 1°, and the model is suitable to calculate slant hydrostatic and wet delays down to 3°elevation at sites in the vicinity of the earth surface using the date and approximate station coordinates as input. The wet delay estimation builds upon gridded values of the water vapor pressure, the weighted mean temperature, and the water vapor decrease factor, with the latter being tuned to ray-traced zenith wet delays. Comparisons with zenith delays at 341 globally distributed global navigation satellite systems stations show that the mean bias over all stations is below 1 mm and the mean standard deviation is about 3.6 cm. The GPT2w model with the gridded input file is provided at http://ggosatm.hg. tuwien.ac.at/DELAY/SOURCE/GPT2w/.

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Section: Validation With Zenith Total Delays From Gnssmentioning

confidence: 99%

Development of an improved empirical model for slant delays in the troposphere (GPT2w)

et al. 2014

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“…These parameters are used to determine values of zenith wet delays, by using the expressions of Saastamoinen (1972), although this approach is not optimal, it represents the starting point for the improved version of it. Thus, the GPT2w as an extension to GPT2 comes with an improved capability to determine zenith wet delays in blind mode Moller et al, 2013;Schingelegger et al, 2014). The…”

Section: Global Pressure Temperature Wet (Gpt2w) Modelmentioning

confidence: 99%

Performance Evaluation of Blind Tropospheric Delay correction Models over Africa

Isioye¹,

Combrinck²,

Botai³

2015

SA J of Geomatics

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“…Böhm et al (2015) proposed the Global Pressure and Temperature 2 wet model (GPT2w) as an extension to GPT2 (Lagler et al, 2013) with an improved capability to determine zenith wet delays in blind model. The GPT2w model accounts for the annual and semiannual variations of meteorological parameters, and the validation with IGS data and an extended validation with raytraced delays (Möller et al, 2014) show a high accuracy of about 3.6 cm for GPT2w. However, GPT2w has numerous parameters for storage like the above grid models such as IGGTrop series models and TropGrid series models.…”

Section: Introductionmentioning

confidence: 98%

An improved global zenith tropospheric delay model GZTD2 considering diurnal variations

Yao

et al. 2016

Nonlin. Processes Geophys.

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Abstract. The zenith tropospheric delay (ZTD) is an important atmospheric parameter in the wide application of global navigation satellite systems (GNSS) technology in geoscience. Given that the temporal resolution of the current global zenith tropospheric delay model (GZTD) is only 24 h, an improved model, GZTD2, has been developed by taking the diurnal variations into consideration and modifying the model expansion function. The data set used to establish this model is the global ZTD grid data provided by Global Geodetic Observing System (GGOS) Atmosphere spanning from 2002 to 2009. We validated the proposed model with respect to ZTD grid data from GGOS Atmosphere, which was not involved in modeling, as well as International GNSS Service (IGS) tropospheric product. The obtained results of ZTD grid data show that the global average bias and root mean square (rms) for the GZTD2 model are 0.2 and 3.8 cm, respectively. The global average bias is comparable to that of the GZTD model, but the global average rms is improved by 3 mm. The bias and rms are far better than the EGNOS model and the UNB series models. The testing results from global IGS tropospheric product show the bias and rms (−0.3 and 3.9 cm) of the GZTD2 model are superior to that of GZTD (−0.3 and 4.2 cm), suggesting higher accuracy and reliability compared to the EGNOS model, as well as the UNB series models.

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Improved Troposphere Blind Models Based on Numerical Weather Data

Cited by 16 publications

References 8 publications

Development of an improved empirical model for slant delays in the troposphere (GPT2w)

Development of an improved empirical model for slant delays in the troposphere (GPT2w)

Performance Evaluation of Blind Tropospheric Delay correction Models over Africa

An improved global zenith tropospheric delay model GZTD2 considering diurnal variations

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