Applications of GNSS Slant Path Delay Data on Meteorology at Storm Scales

Kawabata, Takuya; Shoji, Yasuhiro

doi:10.5772/intechopen.75101

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…Slant wet delay (SWD) or slant water vapor (SWV), which is the wet delay or the total precipitable water vapor in the slant direction, has the ability to capture the water vapor variation in both the horizontal and vertical directions and contains more information than that of the ZWD or PWV [21,22]. Ha, et al [23] assimilated simulative GPS-derived SWD data, which excelled in the reconstruction of water vapor information in a hypothetical network of ground-based GNSS receivers and short-term rainfall prediction.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…The ZTDs can be extracted without overlap, following the resolution of the NWP. Furthermore, SWDs that are especially useful for predicting thunderstorms and/or rainbands (Kawabata and Shoji, 2018) can be separately extracted for those purposes. Regardless of the assimilation details, the 3D distribution of the water vapor can be estimated using tomographic techniques.…”

Section: Gnss-based Tropospheric Estimationmentioning

confidence: 99%

Towards tropospheric delay estimation using GNSS smartphone receiver network

Marques

Mäkelä

Montloin

et al. 2021

Advances in Space Research

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“…Since the observations integrated along the signal's path are asymmetrical, they have the potential to better describe the state of the troposphere. The first studies indicate their capability of obtaining information about storms (Kawabata and Shoji 2018). Although the assimilation of the slant observations into the NWP models is a challenging task, the first results show a positive impact on the weather forecasts (Zus et al 2011(Zus et al , 2015.…”

Section: Introductionmentioning

confidence: 98%

Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution

Trzcina

Rohm

Smolak

2022

J Geod

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Water vapour is a highly variable constituent of the troposphere; thus, its high-resolution measurements are of great importance to weather prediction systems. The Global Navigation Satellite Systems (GNSS) are operationally used in the estimation of the tropospheric state and assimilation of the results into the weather models. One of the GNSS techniques of troposphere sensing is tomography which provides 3-D fields of wet refractivity. The tomographic results have been successfully assimilated into the numerical weather models, showing the great potential of this technique. The GNSS tomography can be based on two different approaches to the parameterisation of the model’s domain, i.e. block (voxel-based) or grid (node-based) approach. Regardless of the parameterisation approach, the tomographic domain should be discretised, which is usually performed in a regular manner, with a grid resolution depending on the mean distance between the GNSS receivers. In this work, we propose a new parameterisation approach based on the optimisation of the tomographic nodes’ location, taking into account the non-uniform distribution of the GNSS information in the troposphere. The experiment was performed using a dense network of 16 low-cost multi-GNSS receivers located in Wrocław and its suburbs, with a mean distance of 3 km. Cross-validation of four different parameterisation approaches is presented. The validation is performed based on the Weather Research and Forecasting model as well as radiosonde observations. The new approach improves the results of wet refractivity estimation by 0.5–2 ppm in terms of RMSE, especially for altitudes of 0.5–2.0 km.

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Applications of GNSS Slant Path Delay Data on Meteorology at Storm Scales

Cited by 4 publications

References 36 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

Towards tropospheric delay estimation using GNSS smartphone receiver network

Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution

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