Modeling tropospheric wet delays with national GNSS reference network in China for BeiDou precise point positioning

Zheng, Fu; Lou, Yidong; Gu, Shengfeng; Gong, Xiaopeng; Shi, Chuang

doi:10.1007/s00190-017-1080-4

Cited by 54 publications

(32 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other empirical troposphere models that may be used in PPP include European Space Agency GAL-TROPO (Martellucci 2012), TropGrid (Kruegger et al, 2004 and its update TopGrid2 (Schüler, 2014). Zheng et al (2017) developed a new troposphere correction model based on GPT2w to reduce PPP convergence time. The RMS of the ZTD with this model was 1.2cm, compared to 3.6cm for GPT2w.…”

Section: Introductionmentioning

confidence: 99%

Comparison of advanced troposphere models for aiding reduction of PPP convergence time in Australia

Deo

El‐Mowafy

2018

Journal of Spatial Science

View full text Add to dashboard Cite

This paper firstly analyses the precision of tropospheric zenith total delay (ZTD) values obtained from the empirical models GPT2 and GPT2w, and the numerical weather models (NWM) from Australian Bureau of Meteorology (BoM), and European Centre for Medium-Range Weather Forecasts (ECMWF). Comparison of these ZTD values with IGS ZTD product at four sites showed that the ZTDs from NWM datasets were more precise than the empirical models. The ZTD from BoM data gave the best results, with mean errors between-0.034m to 0.029m and standard deviations better than 0.045m. Next, the PPP convergence time and achievable accuracy using the BoM NWM constrained ZTD by including them as pseudo-observations with a pre-set precision was compared to the case of estimating the troposphere. This resulted in a slight enhancement in convergence time, and improvements in vertical positioning accuracy was found at all the four tested sites at 0.036-0.058m after 2 minutes, 0.023-0.038m after 3 minutes and 0.013-0.020m after 5 minutes of PPP initialisation.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of advanced troposphere models for aiding reduction of PPP convergence time in Australia

Deo

El‐Mowafy

2018

Journal of Spatial Science

View full text Add to dashboard Cite

show abstract

“…e = h × P/0.622 (8) where k 1 = 77.604 K/Pa, k 2 = 64.79 K/Pa, k 3 = 377,600.0 K 2 /Pa, P represents the atmospheric pressure, h the specific humidity, N the total refraction, and e the vapor pressure. Then the ZTD is calculated by the following formula:…”

Section: Calculating Ztd Using Era5 Meteorological Data At Gnss Stationsmentioning

confidence: 99%

“…Since the distribution of water vapor is irregular, there is no such precise empirical model for Zenith Wet Delay (ZWD). The uncertainties of ZWD affect the convergence time and solution accuracy in GNSS precise positioning and high-precision application [8]. Therefore, the accurate quantification of ZTD is still a challenge owing to the high temporal and spatial variations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Zenith Tropospheric Delay Derived from ERA5 Data over China Using GNSS Observations

Jiang

Wang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is aimed at evaluating the performance of ZTD derived from ERA5 reanalysis data over China using 219 GNSS stations of the Crustal Movement Observation Network of China (CMONOC) covering the period from 2015 to 2016. The site-specific hourly ZTD at these stations is obtained by integration method and Saastamoinen model method on ERA5 pressure-level and surface-level reanalysis data with the temporal resolution of 1 h and the spatial resolution of 0.25° × 0.25°. Firstly, the atmospheric temperature and pressure that derived from ERA5 are compared with temperature and pressure obtained from meteorological sensors available at 193 GNSS stations. The biases are 2.31 °C and 1.26 mbar implying the accuracy and feasibility of ERA5 pressure and temperature for calculating ZTD over China. Secondly, the performance of ERA5 ZTD is systematically evaluated using ZTD from 219 GNSS sites. The average bias and Root Mean Square (RMS) of ERA5 pressure-level ZTD at all test stations in integration method are approximately 2.97 mm and 11.49 mm respectively, and those of ERA5 surface-level ZTD in model method are 7.97 mm, 39.25 mm, which indicates that ERA5 pressure-level ZTD has a higher accuracy over China. Further analysis indicates that the accuracies of ZTD derived from ERA5 pressure-level and surface-level data are approximately 13.8% and 10.9% higher than those from of ERA-Interim pressure-level and surface-level data. Moreover, ERA5 is able to accurately capture the short-term (hourly) variation of ZTD, which further indicates the better performance of ERA5. Thirdly, the temporal and spatial variation characteristics of ERA5 ZTD accuracy are further analyzed over China. The results show that the ZTD in the southern region has the lower accuracy compared with that in the northern region over China due to the influence of latitude and altitude. Furthermore, it is found that the ERA5 ZTD over China has obvious seasonality, with higher accuracy in winter and lower accuracy in summer.

show abstract

“…Lu et al [8] also derived horizontal delay gradients from the NWM model to augment the BeiDou System (BDS) PPP, and more than 30% of the precision improvement in the height component was observed. Zheng et al [9] developed a real-time tropospheric model in China and used it to shorten the convergence of BDS PPP. Douša et al [10] developed a two-stage correction tropospheric model combining data from NWM, precise GNSS ZTDs, and horizontal gradients to support an optimal regional augmentation.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Troposphere Estimated from Galileo-Only Multi-Frequency Observations

2020

View full text Add to dashboard Cite

The tropospheric delays estimated from the Global Navigation Satellite System (GNSS) have been proven to be an efficient product for monitoring variations of water vapor, which plays an important role in meteorology applications. The operational GNSS water vapor monitoring system is currently based on the Global Positioning System (GPS) and GLObal NAvigation Satellite System(GLONASS) dual-frequency observations. The Galileo satellite navigation system has been evolving continuously, and on 11 February 2019, the constellation reached 22 active satellites, achieving a capability of standalone Precise Point Positioning (PPP) and tropospheric estimation that is global in scope. This enhancement shows a 37% improvement if the precision of the Galileo-only zenith tropospheric delay, while we may anticipate further benefits in terms of tropospheric gradients and slant delays in the future if an optimal multi-constellation and multi-frequency processing strategy is used. First, we analyze the performance of the multi-frequency troposphere estimates based on the PPP raw observation model by comparing it with the standard ionosphere-free model. The performance of the Galileo-only tropospheric solution is then validated with respect to GPS-only solution using 48 globally distributed Multi-GNSS Experiment (MGEX) stations. The averaged bias and standard deviations are −0.3 and 5.8 mm when only using GPS satellites, respectively, and 0.0 and 6.2 mm for Galileo, respectively, when compared to the International GNSS Service (IGS) final Zenith Troposphere Delay(ZTD) products. Using receiver antenna phase center corrections from the corresponding GPS dual-frequency observations does not affect the Galileo PPP ambiguity float troposphere solutions. These results demonstrate a comparable precision achieved for both Galileo-only and GPS-only ZTD solutions, however, horizontal tropospheric gradients, estimated from standalone GPS and Galileo solutions, still show larger discrepancies, mainly due to their being less Galileo satellites than GPS satellites. Including Galileo E1, E5a, E5b, and E5 signals, along with their proper observation weighting, show the benefit of multi-frequency observations, further improving the ZTD precision by 4% when compared to the dual-frequency raw observation model. Overall, the presented results demonstrate good prospects for the application of multi-frequency Galileo observations for the tropospheric parameter estimates.

show abstract

Modeling tropospheric wet delays with national GNSS reference network in China for BeiDou precise point positioning

Cited by 54 publications

References 37 publications

Comparison of advanced troposphere models for aiding reduction of PPP convergence time in Australia

Comparison of advanced troposphere models for aiding reduction of PPP convergence time in Australia

Evaluation of Zenith Tropospheric Delay Derived from ERA5 Data over China Using GNSS Observations

Performance Evaluation of Troposphere Estimated from Galileo-Only Multi-Frequency Observations

Contact Info

Product

Resources

About