scholarly journals Development and evaluation of the refined zenith tropospheric delay (ZTD) models

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Fei Yang ◽  
Xiaolin Meng ◽  
Jiming Guo ◽  
Debao Yuan ◽  
Ming Chen

AbstractThe tropospheric delay is a significant error source in Global Navigation Satellite System (GNSS) positioning and navigation. It is usually projected into zenith direction by using a mapping function. It is particularly important to establish a model that can provide stable and accurate Zenith Tropospheric Delay (ZTD). Because of the regional accuracy difference and poor stability of the traditional ZTD models, this paper proposed two methods to refine the Hopfield and Saastamoinen ZTD models. One is by adding annual and semi-annual periodic terms and the other is based on Back-Propagation Artificial Neutral Network (BP-ANN). Using 5-year data from 2011 to 2015 collected at 67 GNSS reference stations in China and its surrounding regions, the four refined models were constructed. The tropospheric products at these GNSS stations were derived from the site-wise Vienna Mapping Function 1 (VMP1). The spatial analysis, temporal analysis, and residual distribution analysis for all the six models were conducted using the data from 2016 to 2017. The results show that the refined models can effectively improve the accuracy compared with the traditional models. For the Hopfield model, the improvement for the Root Mean Square Error (RMSE) and bias reached 24.5/49.7 and 34.0/52.8 mm, respectively. These values became 8.8/26.7 and 14.7/28.8 mm when the Saastamoinen model was refined using the two methods. This exploration is conducive to GNSS navigation and positioning and GNSS meteorology by providing more accurate tropospheric prior information.

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5578
Author(s):  
Fangzhao Zhang ◽  
Jean-Pierre Barriot ◽  
Guochang Xu ◽  
Marania Hopuare

Since Bevis first proposed Global Positioning System (GPS) meteorology in 1992, the precipitable water (PW) estimates retrieved from Global Navigation Satellite System (GNSS) networks with high accuracy have been widely used in many meteorological applications. The proper estimation of GNSS PW can be affected by the GNSS processing strategy as well as the local geographical properties of GNSS sites. To better understand the impact of these factors, we compare PW estimates from two nearby permanent GPS stations (THTI and FAA1) in the tropical Tahiti Island, a basalt shield volcano located in the South Pacific, with a mean slope of 8% and a diameter of 30 km. The altitude difference between the two stations is 86.14 m, and their horizontal distance difference is 2.56 km. In this paper, Bernese GNSS Software Version 5.2 with precise point positioning (PPP) and Vienna mapping function 1 (VMF1) was applied to estimate the zenith tropospheric delay (ZTD), which was compared with the International GNSS Service (IGS) Final products. The meteorological parameters sourced from the European Center for Medium-Range Weather Forecasts (ECMWF) and the local weighted mean temperature ( T m ) model were used to estimate the GPS PW for three years (May 2016 to April 2019). The results show that the differences of PW between two nearby GPS stations is nearly a constant with value 1.73 mm. In our case, this difference is mainly driven by insolation differences, the difference in altitude and the wind being only second factors.


2020 ◽  
Author(s):  
◽  
Juan Manuel Aragón Paz

En el presente trabajo de tesis se desarrolla el diseño e implementación de un sistema de cálculo, en tiempo casi real, de parámetros troposféricos mediante técnicas de navegación global por satélite (GNSS, del inglés Global Navigation Satellite System) para Sudamérica. El desarrollo de la llamada Meteorología GNSS se remonta a principios de la década del 90 donde se encuentran los trabajos fundacionales de esta disciplina. Con el correr de los años, nuevas contribuciones han ido definiendo los reales alcances de esta técnica, poniendo en práctica metodologías cada vez más contrastadas con los métodos de medición tradicionales. En los últimos años los esfuerzos se han enfocado en el desarrollo de procedimientos de cálculo que permitan la utilización de los datos GNSS, cada vez más numerosos, en la asimilación para modelos meteorológicos (en especial los de corto plazo), permitiendo así anticipar eventos con alto impacto a la sociedad civil (tormentas con granizo, inundaciones repentinas, eventos convectivos de mesoescala, etc). Numerosos trabajos se han centrado en la implementación de la meteorología GNSS en Europa, Estados Unidos y Japón. Para la región Sudamericana existen pocos y recientes antecedentes de la aplicación de estas metodologías. Se desarrolló un sistema de cálculo, que permite hacer uso de infraestructura existente en la región, tanto meteorológica como geodésica, enfocado en la obtención de las variables de interés meteorológico como son el retardo troposférico cenital (ZTD, del inglés Zenith Total Delay) y el vapor de agua integrado (IWV, del inglés Integrated Water Vapor). Por otra parte, se han realizado estudios en la aplicación del ZTD y el IWV a índices que permitan dar información rápida acerca de posibles eventos meteorológicos severos. En este trabajo se desarrollan las estrategias diseñadas para la adquisición de los datos, su disponibilidad y alcance. Las problemáticas en la disponibilidad de los mismos, de acuerdo a su procedencia, son descriptas y sorteadas. Seguidamente se brinda una detallada descripción de la metodología de estimación de las observaciones, haciendo especial foco en los parámetros de retardo troposférico cenital (ZTD, del ingles Zenith Tropospheric Delay) y vapor de agua integrado (IWV, del inglés Integrated Water Vapor) mediante el procesamiento de las observaciones GNSS y meteorológicas. Una vez que se tienen los resultados, la presentación de los mismos y los posibles formato de intercambio con las instituciones potenciales usuarias del dato son discutidos. Finalizando esta sección se hace un análisis de la performance del sistema de procesamiento contra las técnicas de radio sondeo (convencionales) y alguno de los modelos de reanálisis mas utilizados. En una segunda etapa se explora las distintas capacidades del IWV GNSS para representar las variaciones temporales y espaciales de la distribución del vapor de agua atmosférico frente a distintas situaciones meteorológicas. También, se describe el desarrollo de posibles índices de alerta que hagan utilización de la información disponible a partir del IWV GNSS. Basándose en bibliografía actualizada se comparan las distintas posibilidades de aplicación a la región de estudio en función de la frecuencia temporal y espacial de las observaciones. Los resultados son presentados analizando un evento de interés meteorológico para la región central de Argentina. Finalmente, los puntos mas salientes del presente trabajo son presentados en las conclusiones. Las mismas abarcan desde el sistema de descarga de datos hasta la implementación de los índices de alerta. Se formulan las posibles derivaciones del trabajo y sus implicancias en la mejora continua de este sistema, que en tiempo casi real, provee información sobre los parámetros de ZTD e IWV. Una sección final describe cuáles son las recomendaciones que permitirían mejoras en la utilización de los datos provistos para conseguir un máximo aprovechamiento de los mismos.


2021 ◽  
Vol 11 (1) ◽  
pp. 14-26
Author(s):  
S. Osah ◽  
A. A. Acheampong ◽  
C. Fosu ◽  
I. Dadzie

Abstract The impact of the earth’s atmospheric layers, particularly the troposphere on Global Navigation satellite system (GNSS) signals has become a major concern in GNSS accurate positioning, navigation, surveillance and timing applications. For precise GNSS applications, tropospheric delay has to be mitigated as accurately as possible using tropospheric delay prediction models. However, the choice of a particular prediction model can signifi-cantly impair the positioning accuracy particularly when the model does not suit the user’s environment. A performance assessment of these prediction models for a suitable one is very important. In this paper, an assessment study of the performances of five blind tropospheric delay prediction models, the UNB3m, EGNOS, GTrop, GPT2w and GPT3 models was conducted in Ghana over six selected Continuously Operating Reference Stations (CORS) using the 1˚x1˚ gridded Vienna Mapping Function 3 (VMF3) zenith tropospheric delay (ZTD) product as a reference. The gridded VMF3-ZTD which is generated for every six hours on the 1˚x1˚ grids was bilinearly interpolated both space and time and transferred from the grid heights to the respective heights of the CORS locations. The results show that the GPT3 model performed better in estimating the ZTD with an overall mean (bias: 2.05 cm; RMS: 2.53 cm), followed by GPT2w model (bias: 2.32cm; RMS: 2.76cm) and GTrop model (bias: 2.41cm; 2.82cm). UNB3m model (bias: 6.23 cm; RMS: 6.43 cm) and EGNOS model (bias: 6.70 cm; RMS: 6.89 cm) performed poorly. A multiple comparison test (MCT) was further performed on the RMSE of each model to check if there is significant difference at 5% significant level. The results show that the GPT3, GPT2w and GTrop models are significantly indifferent at 5% significance level indicating that either of these models can be employed to mitigate the ZTD in the study area, nevertheless, the choice of GPT3 model will be more preferable.


2021 ◽  
Vol 13 (5) ◽  
pp. 838
Author(s):  
Fei Yang ◽  
Jiming Guo ◽  
Chaoyang Zhang ◽  
Yitao Li ◽  
Jun Li

The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the artificial neutral network (ANN) to construct the correlation between ZTD derived from GPT3 and GNSS observations. The experiments in Hong Kong using Satellite Positioning Reference Station Network (SatRet) were conducted and three statistical values, i.e., bias, root mean square error (RMSE), and compound relative error (CRE) were adopted for our comparisons. Numerical results showed that the proposed model outperformed the parameter ZTD model (Saastamoinen model) and the empirical ZTD model (GPT3 model), with an approximately 56%/52% and 52%/37% RMSE improvement in the internal and external accuracy verification, respectively. Moreover, the proposed method effectively improved the systematic deviation of GPT3 model and achieved better ZTD estimation in both rainy and rainless conditions.


2020 ◽  
Vol 55 (4) ◽  
pp. 171-184
Author(s):  
Mohamed Abdelazeem ◽  
Ahmed El-Rabbany

AbstractThis study assesses the precision of zenith tropospheric delay (ZTD) obtained through triple-constellation global navigation satellite system (GNSS) precise point positioning (PPP). Various ZTD estimates are obtained as by-products from GPS-only, GPS/Galileo, GPS/BeiDou, and triple-constellation GPS/Galileo/BeiDou PPP solutions. Triple-constellation GNSS observations from a number of globally distributed reference stations are processed over a period of seven days in order to investigate the daily performance of the ZTD estimates. The estimated ZTDs are then validated by comparing them with the International GNSS Service (IGS) tropospheric products and the University of New Brunswick (UNB3m) model counterparts. It is shown that the ZTD estimates agree with the IGS counterparts with a maximum standard deviation (STD) of 2.4 cm. It is also shown that the precision of estimated ZTD from the GPS/Galileo and GPS/Galileo/BeiDou PPP solutions is improved by about 4.5 and 14%, respectively, with respect to the GPS-only PPP solution. Moreover, it is found that the estimated ZTD agrees with the UNB3m model with a maximum STD of 3.1 cm. Furthermore, the GPS/Galileo and GPS/Galileo/BeiDou PPP enhance the precision of the ZTD estimates by about 6.5 and 10%, respectively, in comparison with the GPS-only PPP solution.


2021 ◽  
Vol 13 (9) ◽  
pp. 1832
Author(s):  
Xiaohui Li ◽  
Dongkai Yang ◽  
Jingsong Yang ◽  
Guoqi Han ◽  
Gang Zheng ◽  
...  

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CyGNSS) mission was launched in December 2016, which can remotely sense sea surface wind with a relatively high spatio-temporal resolution for tracking tropical cyclones. In recent years, with the gradual development of the geophysical model function (GMF) for CyGNSS wind retrieval, different versions of CyGNSS Level 2 products have been released and their performance has gradually improved. This paper presents a comprehensive evaluation of CyGNSS wind product v1.1 produced by the National Oceanic and Atmospheric Administration (NOAA). The Cross-Calibrated Multi-Platform (CCMP) analysis wind (v02.0 and v02.1 near real time) products produced by Remote Sensing Systems (RSS) were used as the reference. Data pairs between the NOAA CyGNSS and RSS CCMP products were processed and evaluated by the bias and standard deviation SD. The CyGNSS dataset covers the period between May 2017 and December 2020. The statistical comparisons show that the bias and SD of CyGNSS relative to CCMP-nonzero collocations when the flag of CCMP winds is nonzero are –0.05 m/s and 1.19 m/s, respectively. The probability density function (PDF) of the CyGNSS winds coincides with that of CCMP-nonzero. Furthermore, the average monthly bias and SD show that CyGNSS wind is consistent and reliable generally. We found that negative deviation mainly appears at high latitudes in both hemispheres. Positive deviation appears in the China Sea, the Arabian Sea, and the west of Africa and South America. Spatial–temporal analysis demonstrates the geographical anomalies in the bias and SD of the CyGNSS winds, confirming that the wind speed bias shows a temporal dependency. The verification and comparison show that the remotely sensed wind speed measurements from NOAA CyGNSS wind product v1.1 are in good agreement with CCMP winds.


2014 ◽  
Vol 20 (3) ◽  
pp. 481-503 ◽  
Author(s):  
TAYNÁ APARECIDA FERREIRA GOUVEIA ◽  
LUIZ FERNANDO SAPUCCI ◽  
JOÃO FRANCISCO GALERA MONICO ◽  
DANIELE BARROCA MARRA ALVES

O posicionamento com o sistema GNSS (Global Navigation Satellite System) é atualmente a técnica mais utilizada para se obter a localização sobre a superfície terrestre ou próxima a essa. Depois dos efeitos causados pela ionosfera, a refração que o sinal sofre ao ultrapassar a neutrosfera pode ser considerada como uma das maiores fontes de erro no sinal, a qual gera um atraso, que rebatida na direção zenital é denominado atraso zenital neutrosférico (ZND), ou ainda atraso zenital troposférico (ZTD - Zenithal Tropospheric Delay). Esse atraso gera erros no posicionamento GNSS quando o mesmo não é devidamente modelado. Os modelos de Previsão Numérica de Tempo (PNT) são boas alternativas para a modelagem do ZND, pois como são alimentados diariamente por observações da atmosfera, os mesmos, geram previsões do ZND capazes de captar suas oscilações espaciais e temporais. No CPTEC/INPE são desenvolvidos e operacionalizados modelos de PNT globais e regionais, sendo os últimos dedicados ao melhor detalhamento sobre a América do Sul. No Brasil está operacional no CPTEC/INPE um processo que gera tais previsões com resolução espacial de 15 km e temporal de 3 horas, além de outras versões que contemplam outras sofisticações. Para determinar o impacto dessas melhorias na qualidade das previsões do ZND, o presente trabalho apresenta uma avaliação robusta das versões disponibilizadas, utilizando como referência os valores de ZND estimados a partir dos dados GNSS coletados pelas estações da RBMC (Rede Brasileira de Monitoramento Contínuo), levando em consideração: a variação sazonal, a continentalidade e a variação da altitude e latitude.


2021 ◽  
Vol 10 (9) ◽  
pp. 623
Author(s):  
Yajie Shi ◽  
Chao Ren ◽  
Zhiheng Yan ◽  
Jianmin Lai

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.


Author(s):  
G. Gurbuz ◽  
K. S. Gormus ◽  
U. Altan

Air pollution is the most important environmental problem in Zonguldak city center. Since bituminous coal is used for domestic heating in houses and generating electricity in thermal power plants, particulate matter (PM<sub>10</sub>) is the leading air pollutant. Previous studies have shown that the water vapor in the troposphere is responsible for the tropospheric zenith delay in Global Navigation Satellite System (GNSS) measurements. In this study, data obtained from the ZONG GNSS station from Türkiye Ulusal Sabit GNSS Ağı (TUSAGA-Active network) in the central district of Zonguldak province, processed with GIPSY-OASIS II and GAMIT/GlobK software using the VMF1 mapping function, which is developed previously and considered to be the most accurate model. The resulting values were examined separately in terms of software. The meteorological parameters obtained from the Turkish State Meteorological Service and the air pollution values obtained from the Ministry of Environment and Urban Planning were analyzed and the zenith delay values were compared. When wet zenith delays of different days with different amounts of PM<sub>10</sub> concentrations were examined in succession and under the same meteorological conditions, differences in the range of 20&amp;ndash;40&amp;thinsp;mm on ZTD were observed.


Sign in / Sign up

Export Citation Format

Share Document