Establishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP

Xia, Pengfei; Tong, Mengxiang; Ye, Shirong; Qian, Jingye; Fangxin, Hu

doi:10.1007/s10291-022-01338-9

Cited by 12 publications

(3 citation statements)

References 40 publications

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“…However, the accuracy is limited due to the low spatial and temporal resolutions of ERA-Interim data. Xia et al [17] employed ECMWF reanalysis v5 (ERA5) data to establish a real-time ZTD model specific to China. Nevertheless, their approach did not comprehensively incorporate the distinct variation patterns across different heights within the troposphere.…”

Section: Introductionmentioning

confidence: 99%

High-precision tropospheric correction method for NRTK regions with significant height differences

Lei,

Xu,

Tao

et al. 2024

Meas. Sci. Technol.

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In response to the issue of poor Network Real-Time Kinematic (NRTK) service performance in regions with significant height differences, an Improved Tropospheric Height Correction (ITHC) method is proposed. Precise Point Positioning (PPP) is employed to compute the troposphere delay at base stations. Subsequently, a Tropospheric Vertical Profile Fitting Model (TVPFM) is established for the vertical reduction of the troposphere in regions with significant height differences. In this case, the tropospheric errors introduced by the height differences between the base and rover stations can be calculated. Finally, the tropospheric error can be corrected during the generation of virtual observations, ensuring high-accuracy positioning of NRTK rovers. With the troposphere delay computed based on the PPP approach, datum errors introduced by inaccurate tropospheric correction methods are mitigated. To reduce the dependence of the troposphere delay on height, ECMWF Reanalysis v5 (ERA5) data are employed to fit the TVPFM. Experimental analysis demonstrates that the troposphere exhibits distinct vertical variation characteristics, allowing for its segmentation into three layers. Consequently, a piecewise TVPFM is established. Observations obtained from the Continuously Operating Reference Stations (CORS) network located in Yunnan, China, are utilized for validation. The selected stations exhibit a maximum height difference of approximately 2 km. The experimental results exhibit a notable enhancement in correction accuracy with the ITHC in comparison to conventional correction methodologies. Specifically, the ambiguity fixing rate demonstrates a noteworthy improvement of 13.3%, accompanied by a substantial increase in positioning accuracy by 51.4%.

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Section: Introductionmentioning

confidence: 99%

High-precision tropospheric correction method for NRTK regions with significant height differences

Lei,

Xu,

Tao

et al. 2024

Meas. Sci. Technol.

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“…In Global Navigation Satellite System (GNSS) data processing, the delay on the signal path is generally calculated using the product of the Zenith Tropospheric Delay (ZTD) and the projection function related to the altitude angle [2,3]. On the one hand, tropospheric delay is an important error source in the navigation and positioning process of GNSS [4,5]; on the other hand, the water vapor information it contains is the basis for water vapor retrieval in GNSS meteorology [6,7]. Therefore, establishing a stable and reliable ZTD model is one of the current GNSS research hot spots.…”

Section: Introductionmentioning

confidence: 99%

A Refined Zenith Tropospheric Delay Model Based on a Generalized Regression Neural Network and the GPT3 Model in Europe

Wei,

Yu,

et al. 2023

Atmosphere

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An accurate model of the Zenith Tropospheric Delay (ZTD) plays a crucial role in Global Navigation Satellite System (GNSS) precise positioning, water vapor retrieval, and meteorological research. Current empirical models (such as the GPT3 model) can only reflect the approximate change trend of ZTD but cannot accurately reflect nonlinear changes such as rapid fluctuations in ZTD. In recent years, the application of machine learning methods in the modeling and prediction of ZTD has gained prominence, yielding commendable results. Utilizing the ZTD products from 53 International GNSS Service (IGS) stations in Europe during the year 2021 as a foundational dataset, a Generalized Regression Neural Network (GRNN) is employed to model IGS ZTD while considering spatiotemporal factors and its association with GPT3 ZTD. This endeavor culminates in the development of a refined GRNN model. To verify the performance of the model, the prediction results are compared with two other ZTD values. One is obtained based on the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data, and the other is obtained by the GPT3 model. The results show that the bias of the GRNN refined model is almost 0 mm, and the average Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE) are 18.33 mm and 14.08 mm, respectively. Compared with ERA5 ZTD and GPT3 ZTD, the RMSE of GRNN ZTD has decreased by 19.5% and 63.4%, respectively, and the MAE of GRNN ZTD has decreased by 24.8% and 67.1%. Compared with the other two models, the GRNN refined model has better performance in reflecting the rapid fluctuations of ZTD. In addition, also discussed is the impact of spatial factors and time factors on modeling. The findings indicate that modeling accuracy within the central region of the modeling area surpasses that at the periphery by approximately 17.8%. The period from June to October is associated with the lowest accuracy, whereas the optimal accuracy is typically observed from January to April. The most substantial differences in accuracy were observed at station OP71 (Paris, France), with the highest accuracy recorded (9.51 mm) in April and the lowest (24.00 mm) in September.

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“…Zenith tropospheric delay (ZTD) is a crucial error source in global navigation satellite system (GNSS) positioning. An accurate prior ZTD is beneficial for positioning accuracy and convergence time in precise point positioning (PPP) [1][2][3][4][5][6][7][8][9]. ZTD has two components: zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD) [10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay

Li,

Liu

et al. 2023

Remote Sensing

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An accurate estimation of zenith wet delay (ZWD) is crucial for global navigation satellite system (GNSS) positioning and GNSS-based precipitable water vapor (PWV) inversion. The forecast Vienna Mapping Function 3 (VMF3-FC) is a forecast product provided by the Vienna Mapping Functions (VMF) data server based on the European Centre for Medium-Range Weather Forecasts (ECMWF)-based numerical weather prediction (NWP) model. The VMF3-FC can provide ZWD at any time and for any location worldwide; however, it has an uneven accuracy distribution and fails to match the application requirements in certain areas. To address this issue, in this study, a calibrated model for VMF3-FC ZWD, named the XZWD model, was developed by utilizing observation data from 492 radiosonde sites globally from 2019–2021 and the eXtreme Gradient Boosting (XGBoost) algorithm. The performance of the XZWD model was validated using 2022 observation data from the 492 radiosonde sites. The XZWD model yields a mean bias of −0.03 cm and a root-mean-square error (RMSE) of 1.64 cm. The XZWD model outperforms the global pressure and temperature 3 (GPT3) model, reducing the bias and RMSE by 94.64% and 58.90%, respectively. Meanwhile, the XZWD model outperforms VMF3-FC, with a reduction of 92.68% and 6.29% in bias and RMSE, respectively. Furthermore, the XZWD model reduces the impact of ZWD accuracy by latitude, height, and seasonal variations more effectively than the GPT3 model and VMF3-FC. Therefore, the XZWD model yields higher stability and accuracy in global ZWD forecasting.

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Establishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP

Cited by 12 publications

References 40 publications

High-precision tropospheric correction method for NRTK regions with significant height differences

High-precision tropospheric correction method for NRTK regions with significant height differences

A Refined Zenith Tropospheric Delay Model Based on a Generalized Regression Neural Network and the GPT3 Model in Europe

Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay

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