Analysing the Zenith Tropospheric Delay Estimates in On-line Precise Point Positioning (PPP) Services and PPP Software Packages

Méndez-Astudillo, Jorge; Lau, Lawrence; Tang, Yu-Ting; Moore, Terry

doi:10.3390/s18020580

Cited by 45 publications

(37 citation statements)

References 24 publications

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“…11 it is evident that no clear relationship exists between the RMS and station latitudes. The results in this study however, do not match with those reported by others such as Lu et al (2016) and Astudillo et al (2018). Therefore, the study on the quality of ZTD estimation at different latitudes needs to be further confirmed with much denser GPS network in the future.…”

Section: Comparison Between Gogps and Apps Ztdcontrasting

confidence: 93%

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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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Section: Comparison Between Gogps and Apps Ztdcontrasting

confidence: 93%

“…There is an increasing use of PPP for processing the GNSS data using different software packages that have been developed (Astudillo et al, 2018). However, the main problem of using the PPP method is the long interval of about 20-30 min required for the solution convergence (Dousa and Vaclavovic, 2014;Wonnacott and Merry, 2006).…”

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 PPP positioning technique is widely implemented in many software and online services. A previous study [19] found that most online software available for PPP obtained very close values for the estimation of the ZTD. Thus, we can expect consistent results using any software.…”

Section: Introductionmentioning

confidence: 90%

A Novel Approach for the Determination of the Height of the Tropopause from Ground-Based GNSS Observations

et al. 2020

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In this paper, we present a new method to calculate the height of the second lapse-rate tropopause (LRT2) using GNSS high-precision data. The use of GNSS data for monitoring the atmosphere is possible because as the radio signals propagate through the troposphere, they are delayed according to the refractive index of the path of the signal. We show that by integrating the vertical profile of the refractive index in the troposphere, we are able to determine the altitude of LTR2. Furthermore, as GNSS data is available from many stations around all latitudes of the globe and make up a network with high spatial and temporal resolution, we can monitor the diurnal cycle of the variables related to the refractive index of the path of the signal. A comparison between the heights of the LRT2 obtained with radiosonde data and with this novel method is presented in the paper, and it shows good agreement. The average difference found is ≤1 km for stations between the latitudes of 30°S and 30°N.

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“…The VMF1 model derived hydrostatic delays are provided in the Global Geodetic Observing System (GGOS) website [22] and are based on meteorological data from Numerical Weather Models (NWMs). ZHD can also be derived using a static model that is based on the station height only [23]. Static models are more appropriate for tropical stations, where temperature variations are minimal.…”

Section: A Gps-derived Water Vapor Contentmentioning

confidence: 99%

A Data-Driven Approach for Accurate Rainfall Prediction

Manandhar

Dev

Lee

et al. 2019

IEEE Trans. Geosci. Remote Sensing

107

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In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a four-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

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Analysing the Zenith Tropospheric Delay Estimates in On-line Precise Point Positioning (PPP) Services and PPP Software Packages

Cited by 45 publications

References 24 publications

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

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

A Novel Approach for the Determination of the Height of the Tropopause from Ground-Based GNSS Observations

A Data-Driven Approach for Accurate Rainfall Prediction

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