Improving GNSS Zenith Wet Delay Interpolation by Utilizing Tropospheric Gradients: Experiments with a Dense Station Network in Central Europe in the Warm Season

Zus, Florian; Douša, Jan; Kačmařík, Michal; Václavovic, Pavel; Balidakis, Kyriakos; Dick, Galina; Wickert, Jens

doi:10.3390/rs11060674

Cited by 34 publications

(19 citation statements)

References 34 publications

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“…Chen and Herring [50] and Herring [56] proposed a gradient model based on the "tilted" atmosphere assumption, which can be expressed as the first term of the gradient mapping function [67]:…”

Section: Gradient Modelmentioning

confidence: 99%

The Performance of Different Mapping Functions and Gradient Models in the Determination of Slant Tropospheric Delay

Qiu

Wang

et al. 2020

Remote Sensing

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Global navigation satellite systems (GNSSs) have become an important tool for remotely sensing water vapor in the atmosphere. In GNSS data processing, mapping functions and gradient models are needed to map the zenith tropospheric delay (ZTD) to the slant total tropospheric delay (STD) along a signal path. Therefore, it is essential to investigate the spatial–temporal performance of various mapping functions and gradient models in the determination of STD. In this study, the STDs at nine elevations were first calculated by applying the ray-tracing method to the atmospheric European Reanalysis-Interim (ERA—Interim) dataset. These STDs were then used as the reference to study the accuracy of the STDs that determined the ZTD together with mapping functions and gradient models. The performance of three mapping functions (i.e., Niell mapping function (NMF), global mapping function (GMF), and Vienna mapping function (VMF1)) and three gradient models (i.e., Chen, MacMillan, and Meindl) in six regions (the temperate zone, Qinghai–Tibet Plateau, Equator, Sahara Desert, Amazon Rainforest, and North Pole) in determining slant tropospheric delay was investigated in this study. The results indicate that the three mapping functions have relatively similar performance above a 15° elevation, but below a 15° elevation, VMF1 clearly performed better than the GMF and NMF. The results also show that, if no gradient model is included, the root-mean-square (RMS) of the STD is smaller than 2 mm above the 30° elevation and smaller than 9 mm above the 15° elevation but shows a significant increase below the 15° elevation. For example, in the temperate zone, the RMS increases from approximately 35 mm at the 10° elevation to approximately 160 mm at the 3° elevation. The inclusion of gradient models can significantly improve the accuracy of STDs by 50%. All three gradient models performed similarly at all elevations and in all regions. The bending effect was also investigated, and the results indicate that the tropospheric delay caused by the bending effect is normally below 13 mm above a 15° elevation, but this delay increases dramatically from approximately 40 mm at a 10° elevation to approximately 200 mm at a 5° elevation, and even reaches 500–700 mm at a 3° elevation in most studied regions.

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Section: Gradient Modelmentioning

confidence: 99%

The Performance of Different Mapping Functions and Gradient Models in the Determination of Slant Tropospheric Delay

Qiu

Wang

et al. 2020

Remote Sensing

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“…GAMIT are based on Double difference mode, and GPAS are based on Non-difference mode. They treat the parameterization of ZTD in the similar way, and both consider the tropospheric delay Gradients to improve the accuracy of ZTD [37]. Meanwhile, both software can further calculate ZWD by removing the ZHD calculated by the modified Saastamoinen model from ZTD.…”

Section: A Tropospheric Zenith Wet Delaymentioning

confidence: 99%

Short-Term PM2.5 Concentration Prediction by Combining GNSS and Meteorological Factors

Wen

Dang

Li³

2020

IEEE Access

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With the development of industrialization, fine particulate matter (PM2.5) severely chills the health of people. Studies have shown that the variation of PM2.5 concentration is related to the Global Navigation Satellite System (GNSS) tropospheric delay. Therefore, it is possible to use the widely distributed continuous operation reference station (CORS) to monitor and predict PM2.5 concentrations with high time resolution. In this paper, the zenith wet delay (ZWD) of five CORS located in Baoding, Hebei Province, China, from Sep 2014 to Feb 2015, is calculated firstly. Then, the correlation between PM2.5 and ZWD is investigated. Finally, the experimental data including PM2.5, ZWD, temperature, air pressure, and relative humidity data sampled in one hour are used to establish PM2.5 concentrations online prediction model based on support vector machine regression (SVMR) model with metabolic method. The experimental results show that in autumn and winter, the correlation coefficient between daily-mean PM2.5 and ZWD is mainly larger than 0.4, and the correlation coefficient between hourly mean PM2.5 and ZWD is mainly larger than 0.3. Meanwhile, in daily cycle analysis, air temperature, air pressure and relative humidity are related to PM2.5 concentration. Finally, Using SVMR model with metabolic method, by combining GNSS and meteorological factors, ideal short-term prediction accuracy is achieved, which shows that the use of GNSS and meteorological factors is potential in predicting PM2.5 concentration.

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“…From ther esults shown in Section 8.6,n ob eneficial effect could be found on ZTDs olutionsw hen extending the parameter estimationf or gradients. This disagrees withl iteraturea vailableu pt ill nowo nt his topic (Zus et al (2019a), Hadas et al (2020). N evertheless, one has to keep in mind thatt hese studies focused on static measurements, mostly fromh igh-quality data gathered at reference stations placed strategically.H owever, thei nitials ituation fort he data usedi nt his study is significantly different.…”

Section: Extension To Advanced Troposphericp Arametersmentioning

confidence: 85%

“…In terms of data delivery, the current setup couldbeextended foradvanced tropospheric products, i.eslant delays and horizontal gradients. Estimationofhorizontalgradientswas alreadyi nvestigated in the course of this thesis, see Section 8.6.G radient estimation has shown to be beneficial forG NSS-ZTDq uality ( Zus et al (2019a), Hadase ta l.…”

Section: Extension To Advanced Troposphericp Arametersmentioning

confidence: 97%

Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains

Aichinger-Rosenberger

Hanna²,

Weber³

2021

Preprint

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<p>Electromagnetic signals, as broadcasted by Global Navigation Satellite Systems (GNSS), are delayed when travelling through the Earth&#8217;s atmosphere due to the presence of water vapour. Parametrisations of this delay, most prominently the Zenith Total Delay (ZTD) parameter, have been studied extensively and proven to provide substantial benefits for atmospheric research and especially the Numerical Weather Prediction (NWP) model performance. Typically, regional/global networks of static reference stations are utilized to derive ZTD along with other parameters of interest in GNSS analysis (e.g. station coordinates). Results are typically used as a contributing data source for determining the initial conditions of NWP models, a process referred to as Data Assimilation (DA).</p><p>This contribution goes beyond the approach outlined above as it shows how reasonable tropospheric parameters can be derived from highly kinematic, single-frequency (SF) GNSS data. The utilized data was gathered at trains by the Austrian Federal Railways (&#214;BB) and processed using the Precise Point Positioning (PPP) technique. Although the special nature of the observations yields several challenges concerning data processing, we show that reasonable results for ZTD estimates can be obtained for all four analysed test cases by using different PPP processing strategies. Comparison with ZTD calculated from ERA5 reanalysis data yields a very high correlation and an overall agreement from the low millimetre-range up to 5 cm, depending on solution and analysed travelling track. We also present the first tests of assimilation into a numerical weather prediction (NWP) model which show the reasonable quality of the results as between 30-100 % of the observations are accepted by the model. Furthermore, we investigate means to transfer the developed ideas to an operational stage in order to exploit the huge benefits (horizontal/temporal resolution) of this special dataset for operational weather forecasting.&#160;</p>

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Improving GNSS Zenith Wet Delay Interpolation by Utilizing Tropospheric Gradients: Experiments with a Dense Station Network in Central Europe in the Warm Season

Cited by 34 publications

References 34 publications

The Performance of Different Mapping Functions and Gradient Models in the Determination of Slant Tropospheric Delay

The Performance of Different Mapping Functions and Gradient Models in the Determination of Slant Tropospheric Delay

Short-Term PM2.5 Concentration Prediction by Combining GNSS and Meteorological Factors

Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains

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