Single station GNSS tomography as a replacement of mapping function for high precision troposphere radio wave delay correction

2020 ◽  
Author(s):  
Feng Peng ◽  
Li Fei ◽  
Jean-Pierre Barriot ◽  
Yan Jianguo ◽  
Zhang Fangzhao ◽  
...  
Keyword(s):  
Radio Wave ◽  
Time Resolution ◽  
Mapping Function ◽  
Satellite Tracking ◽  
Tropospheric Delay ◽  
High Time Resolution ◽  
Signal Delay ◽  
Mapping Functions ◽  
Single Station ◽  
Delay Correction

<p>With its relatively low cost, high availability and continuous observation ability, zenith delays from GPS combined with mapping function have been used in satellite tracking media calibration since early 2000. The mapping functions are used to model elevation dependency of radio wave delays in the troposphere. It assumes that the ratio of signal slant delay over zenith delay is less variable w.r.t time and location than the signal delay itself. Thus the parameters of signal delay elevation dependency can be modeled and unknowns of the tropospheric delay were reduced. However, the parameterization comes with a loss of accuracy. For example, the state-of-art VMF series mapping functions have a time resolution of 6 hours, which means variations that took place in less than 6 hours are smoothed. Nowadays GPS has evolved to multi-constellation GNSS with many more satellites in visibility. Here we propose a single station GNSS tomography algorithm for radio wave delay correction by directly using slant delays. This algorithm can extract the information of the troposphere variations in all the signal directions of GNSS observations with high time resolution. Thus it will be beneficial to the radio wave delay correction of precise satellite tracking. We assess the performance of this algorithm with a collocated water vapor radiometer.</p>

scholarly journals Evaluation of Wet Mapping Functions Used in Modeling Tropospheric Propagation Delay Effect on GPS Measurements

2017 ◽  
Vol 11 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Sobhy Abdel-Monam Younes
Keyword(s):  
A Priori ◽  
Mapping Function ◽  
Propagation Delay ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Delay Effect ◽  
Gps Measurements ◽  
Mapping Functions ◽  
Significant Bias ◽  
Mapping Techniques

Background:The author compares several methods to map the a priori wet tropospheric delay of GNSS signals in Egypt from the zenith direction to lower elevations.Methods and Materials:The author compared the following mapping techniques against ray-traced delays computed for radiosonde profiles under the assumption of spherical symmetry: Saastamoinen, Hopfield, Black, Chao, Ifadis, Herring, Niell, Moffett, Black and Eisner and UNBabc mapping functions. Radiosonde data were computed from radiosonde stations at the Egyptian stations; in the south of Egypt, near the Mediterranean Sea, and near the Red Sea over a period of 5 years (2000-2005), most of the stations launched radiosonde twice daily, every day of the year. Moreover, data is received from the Egyptian Meteorology Authority.Results and Conclusion:The results indicate that currently, the saastamoinen mapping function should be used for all geodetic applications in Egypt, and if necessary, the Chao and Moffett mapping functions can serve as an acceptable replacement without introducing a significant bias into the station position.

scholarly journals Evaluation of Symmetric Neutral-Atmosphere Mapping Functions Using Ray-Tracing Through Radiosonde Observations

2013 ◽  
Vol 48 (4) ◽  
pp. 171-189
Author(s):  
A.H. Souri ◽  
M.A. Sharifi
Keyword(s):  
Ray Tracing ◽  
Elevation Angle ◽  
Mapping Function ◽  
Ground Truth ◽  
Ease Of Use ◽  
Tropospheric Delay ◽  
Neutral Atmosphere ◽  
Mapping Functions ◽  
Climatic Regions ◽  
Online Mapping

Abstract The aim of this paper is to compare the validity of six recent symmetric mapping functions. The mapping function models the elevation angle dependence of the tropospheric delay. Niell Mapping Function (NMF), Vienna Mapping Function (VMF1), University of New Brunswick- VMF1 (UNB-VMF1) mapping functions, Global Mapping Function (GMF) and Global Pressure and Temperature (GPT2)/GMF are evaluated by using ray tracing through 25 radiosonde stations covering different climatic regions in one year. The ray-traced measurements are regarded as “ground truth”. The ray-tracing approach is performed for diverse elevation angle starting at 5° to 15°. The results for both hydrostatic and non-hydrostatic components of mapping functions support the efficiency of online-mapping functions. The latitudinal dependence of standard deviation for 5° is also demonstrated. Although all the tested mapping functions can provide satisfactory results when used for elevation angles above 15°, for high precision geodetic measurements, it is highly recommended that the online-mapping functions (UNBs and VMF1) be used.The results suggest that UNB models, like VMF have strengths and weaknesses and do not stand out as being consistently better or worse than the VMF1. The GPT2/GMF provided better accuracy than GMF and NMF. Since all of them do not require site specific data; therefore GPT2/GMF can be useful as regards its ease of use.

The Effect of the State-of-the-Art Mapping Functions on Precise Point Positioning

2020 ◽  
Author(s):  
Faruk Can Durmus ◽  
Bahattin Erdogan
Keyword(s):  
Water Vapor ◽  
Precise Point Positioning ◽  
State Of The Art ◽  
Mapping Function ◽  
Empirical Models ◽  
Global Navigation Satellite Systems ◽  
Tropospheric Delay ◽  
Positioning Accuracy ◽  
Mapping Functions ◽  
Point Positioning

<p>Global Navigation Satellite Systems (GNSS) are effectively used for different applications of Geomatic Engineering. There are lots of model error sources that affect the performance of the point positioning. Especially for the Precise Point Positioning (PPP) technique, which depends on the absolute point positioning, these errors should be modelled since PPP technique utilizes un-differenced and ionosphere-free combinations. Studies about PPP technique show that the effect of tropospheric delay caused by water vapor and dry air in the troposphere, which affects GNSS signals, is an important parameter should be modelled. Total zenith delay consists of both hydrostatic and wet delay. Hydrostatic delay can be accurately estimated by using atmospheric surface pressure and temperature with empirical models. Although there are many empirical models currently used for the determination of the zenith wet delay, the accuracies of these models are inadequate due to the temporal and spatial variation of atmospheric water vapor. Moreover, the tropospheric delay occurs along the path of GNSS signals and the Mapping Functions (MFs) are used to convert the tropospheric signal delay along the zenith direction to the slant direction. In this study, it is aimed to measure the effect of the globally produced MFs as Niell Mapping Function (NMF), Vienna Mapping Function 1 (VMF1), Global Mapping Function (GMF) and Global Pressure Temperature model 2 (GPT2) for GNSS positioning accuracy. Only GPS satellite system has been taken into account. For the analysis it has planned to process approximately 294 permanent stations from Crustal Dynamics Data Information System (CDDIS) archive with Jet Propulsion Laboratory’s GipsyX v1.2 software. In order to reveal the effect of different season the GPS observations in January, April, July and October, 2018 have been obtained. The solutions were derived for different session durations as 2, 4, 6, 8, 12 and 24 hours for each global MFs and root mean square values have been estimated for each session durations. According to the first results that based on the six points, which the ellipsoidal heights of them are between 20 m and 105 m, although the results of north and east components are close to each other; the results of VMF1 are better than other global MFs for up component.</p><p> </p><p><strong>Keywords</strong>: State-of-the-Art Mapping Function, Troposphere, Precise Point Positioning, Accuracy, GipsyX</p>

scholarly journals Using Different Mapping Function In GPS Processing For Remote Sensing The Atmosphere

2015 ◽  
Vol 5 (2) ◽  
pp. 73-80 ◽  
Author(s):  
S. Nistor ◽  
A.S. Buda
Keyword(s):  
Mapping Function ◽  
Order Effect ◽  
Tropospheric Delay ◽  
Mapping Functions ◽  
Zenith Tropospheric Delay ◽  
Zenith Wet Delay ◽  
Different Types ◽  
Gps Processing ◽  
Zenith Hydrostatic Delay ◽  
L1 And L2

Abstract Due to development of GPS technology and by using the combination LC of L1 and L2 frequency the first order effect of the ionosphere tends to be canceled. Thus the main source of errors in the atmosphere which causes the delay in GPS signal is the neutral part of the atmosphere, usually referred to tropospheric delay. In general, the delay is computed at the zenith direction and it is referred to zenith tropospheric delay. The zenith tropospheric delay consist of two parts: zenith hydrostatic delay and zenith wet delay. The zenith hydrostatic delay can be very well modeled which accounts for nearly 90% to 100% of the atmospheric delay. The zenith wet delay is due to the water vapor and represents the “harder” part that need to be modeled caused by “unmixed” condition of the wet atmosphere. The influence of the zenith wet delay is around 0-40 cm. The aim of the article is to present the results obtain on the network of three station which were spread around the Oradea city using different types of mapping functions. The mapping functions are: global pressure and temperature – GPT2 and Vienna mapping function – VMF1. For the vertical studies to obtain the highest accuracy, the recommended mapping function is VMF1.

scholarly journals A comprehensive comparison of atmospheric mapping functions for GPS measurements in Egypt

2012 ◽  
Vol 2 (3) ◽  
pp. 216-223 ◽  
Author(s):  
S. A. Younes ◽  
A. G. Elmezayen
Keyword(s):  
Meteorological Data ◽  
Mapping Function ◽  
Propagation Delay ◽  
Tropospheric Delay ◽  
Radio Waves ◽  
Dual Frequency ◽  
Mapping Functions ◽  
Limiting Error ◽  
Comprehensive Comparison ◽  
Delay Prediction

AbstractThe principal limiting error source in the Global Positioning System (GPS) is the mismodeling of the delay experienced by radio waves in propagating through the atmosphere. The atmosphere causing the delay in GPS signals consists of two main layers: the ionosphere and the troposphere. The ionospheric delay can be mitigated using dual frequency receivers, but the tropospheric delay is often corrected using a standard tropospheric model. The tropospheric delay can be described as a product of the delay at the zenith and a mapping function, which models the elevation dependence of the propagation delay. A large number of mapping functions have been developed for use in the analysis of space geodetic data. An assessment of most of these mapping functions including those developed by Niell (NMF), Herring (MTT), Davis (CfA-2.2), Ifadis, Chao, Black & Eisner (B & E), Yang & Ping, Moffett, Vienna (VMF), and Isobaric (IMF) have been performed. The behavior of these mapping functions was assessed by comparing their results with highly accurate Numerical Integration based Models (NIM) for three different stations in Egypt (Aswan, Helwan, and Mersa Matrouh) at different times throughout the year. The meteorological data used in this study was taken from the Egyptian Meteorological Authority (EMA) as average values between 1990 and 2005. It can be concluded that the Black & Eisner mapping function is recommended for dry tropospheric delay prediction for low zenith angles, whereas VMF will be the choice for elevation angles up to 10°.

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

2020 ◽  
Vol 12 (1) ◽  
pp. 130 ◽  
Author(s):  
Cong Qiu ◽  
Xiaoming Wang ◽  
Zishen Li ◽  
Shaotian Zhang ◽  
Haobo Li ◽  
...  
Keyword(s):  
Mapping Function ◽  
Temperate Zone ◽  
Global Navigation Satellite Systems ◽  
Tibet Plateau ◽  
Tropospheric Delay ◽  
Mapping Functions ◽  
Satellite Systems ◽  
Bending Effect ◽  
Gradient Models

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.

The Effect of the State-of-the-Art Mapping Functions on Precise Point Positioning

2021 ◽  
Author(s):  
Faruk Can Durmus ◽  
Bahattin Erdogan
Keyword(s):  
Water Vapor ◽  
Precise Point Positioning ◽  
State Of The Art ◽  
Mapping Function ◽  
Empirical Models ◽  
Global Navigation Satellite Systems ◽  
Tropospheric Delay ◽  
Positioning Accuracy ◽  
Mapping Functions ◽  
Point Positioning

<p>Global Navigation Satellite Systems (GNSS) are effectively used for different applications of Geomatic Engineering. There are lots of model error sources that affect the performance of the point positioning. Especially for the Precise Point Positioning (PPP) technique, which depends on the absolute point positioning, these errors should be modelled since PPP technique utilizes un-differenced and ionosphere-free combinations. Studies about PPP technique show that the effect of tropospheric delay caused by water vapor and dry air in the troposphere, which affects GNSS signals, is an important parameter should be modelled. Total zenith delay consists of both hydrostatic and wet delay. Hydrostatic delay can be accurately estimated by using atmospheric surface pressure and height with empirical models. Although there are many empirical models currently used for the determination of the zenith wet delay, the accuracies of these models are inadequate due to the temporal and spatial variation of atmospheric water vapor. Moreover, the tropospheric delay occurs along the path of GNSS signals and the Mapping Functions (MFs) are used to convert the tropospheric signal delay along the zenith direction to the slant direction. In this study, it is aimed to measure the effect of the globally produced MFs as Niell Mapping Function (NMF), Vienna Mapping Function 1 (VMF1), Global Mapping Function (GMF) and Global Pressure Temperature model 2 (GPT2) for GNSS positioning accuracy. Only GPS satellite system has been taken into account. For the analysis it has planned to process approximately 294 permanent stations from Crustal Dynamics Data Information System (CDDIS) archive with Jet Propulsion Laboratory’s GipsyX v1.2 software. In order to reveal the effect of different season the GPS observations in January, April, July and October, 2018 have been obtained. The solutions were derived for different session durations as 2, 4, 6, 8, 12 and 24 hours for each global MFs and root mean square values have been estimated for each session durations.</p><p><strong>Keywords</strong>: State-of-the-Art Mapping Function, Troposphere, Precise Point Positioning, Accuracy, GipsyX</p>

Solar Corona Investigation by the Coronal Line Photometer at Lomnický Peak

1994 ◽  
Vol 144 ◽  
pp. 431-434
Author(s):  
M. Minarovjech ◽  
M. Rybanský
Keyword(s):  
Solar Corona ◽  
Time Resolution ◽  
Active Regions ◽  
High Time ◽  
High Time Resolution ◽  
List Type ◽  
Type Number ◽  
Coronal Line ◽  
Global Cycle

AbstractThis paper deals with a possibility to use the ground-based method of observation in order to solve basic problems connected with the solar corona research. Namely:1.heating of the solar corona2.course of the global cycle in the corona3.rotation of the solar corona and development of active regions.There is stressed a possibility of high-time resolution of the coronal line photometer at Lomnický Peak coronal station, and use of the latter to obtain crucial observations.

Periodic Variations in the Coronal Green Line Intensity and their Connection with the White-light Coronal Structures

2000 ◽  
Vol 179 ◽  
pp. 197-200
Author(s):  
Milan Minarovjech ◽  
Milan Rybanský ◽  
Vojtech Rušin
Keyword(s):  
Time Resolution ◽  
White Light ◽  
Short Time Scale ◽  
High Time Resolution ◽  
Green Line ◽  
Periodic Intensity ◽  
Coronal Green Line ◽  
Significant Intensity ◽  
Short Time ◽  
Coronal Structures

AbstractWe present an analysis of short time-scale intensity variations in the coronal green line as obtained with high time resolution observations. The observed data can be divided into two groups. The first one shows periodic intensity variations with a period of 5 min. the second one does not show any significant intensity variations. We studied the relation between regions of coronal intensity oscillations and the shape of white-light coronal structures. We found that the coronal green-line oscillations occur mainly in regions where open white-light coronal structures are located.

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