Assessment of GNSS zenith tropospheric delay responses to atmospheric variables derived from ERA5 data over Nigeria

Nzelibe, Ifechukwu Ugochukwu; Herbert, Tata; Idowu, Timothy Oluwadare

doi:10.1186/s43020-023-00104-7

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…It provides real-time and high-precision deformations, facilitating effective analysis and predictions. However, the complex environment around hydraulic structures leads to measurement errors, such as atmospheric delays [11][12][13] and multipath effects [14][15][16][17]. These errors are difficult to process, which adds obstacles to the prediction Appl.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Xie,

Wang,

et al. 2024

Applied Sciences

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Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

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Section: Introductionmentioning

confidence: 99%

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Xie,

Wang,

et al. 2024

Applied Sciences

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“…With an average of root mean squares error of roughly 12 mm, the ZTD showed great agreement with IGS products. Nzelibe et al [27] proved a great improvement in accuracy in ZTD estimation for GNSS positioning when using the ERA5 atmospheric variables. Zhang et al [28] proposed a method to obtain better ZHD corrections to solve the issues with the application of the conventional approach in areas with high variations in height, and their results showed that the method can achieve an improvement in accuracy of 50% over the conventional approach.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations

Xia,

Jin,

Jin

2023

Remote Sensing

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Multi-Global Navigation Satellite Systems (multi-GNSS) (including GPS, BDS, Galileo, and GLONASS) provide a significant opportunity for high-quality zenith tropospheric delay estimation and its applications in meteorology. However, the performance of zenith total delay (ZTD) retrieval from single- or multi-GNSS observations is not clear, particularly from the new, fully operating BDS-3. In this paper, zenith tropospheric delay is estimated using the single-, dual-, triple-, or four-GNSS Precise Point Positioning (PPP) technique from 55 Multi-GNSS Experiment (MGEX) stations over one year. The performance of GNSS ZTD estimation is evaluated using the International GNSS Service (IGS) standard tropospheric products, radiosonde, and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5). The results show that the GPS-derived ZTD time series is more consistent and reliable than those derived from BDS-only, Galileo-only, and GLONASS-only solutions. The performance of the single-GNSS ZTD solution can be enhanced with better accuracy and stability by combining multi-GNSS observations. The accuracy of the ZTD from multi-GNSS observations is improved by 13.8%, 43.8%, 27.6%, and 22.9% with respect to IGS products for the single-system solution (GPS, BDS, Galileo, and GLONASS), respectively. The ZTD from multi-GNSS observations presents higher accuracy and a significant improvement with respect to radiosonde and ERA5 data when compared to the single-system solution.

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“…With the continuous improvement of these meteorological empirical models, some scholars have combined meteorological empirical models with zenith tropospheric delay models, such as the GPT3 + Saastamoinen method, which calculates the average deviation of ZTD at more than 16,000 global stations over 10 years at around −0.99 cm [26]. At present, some scholars have also combined numerical reanalysis data with tropospheric delay models and have achieved good results in improving the accuracy of prior values in precise models and in improving the positioning accuracy as well [7,[27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Tropospheric Delay Parameter Estimation Strategy in BDS Precise Point Positioning

Liu

et al. 2023

Remote Sensing

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Tropospheric delay (TD) parameter estimation is a critical issue underlying high-precision data processing for global navigation satellite systems (GNSSs). The most widely used TD parameter estimation methods are the random walk (RW) and piece-wise constant (PWC). The RW method can effectively track rapid variations of tropospheric delay, but it may introduce excessive noise. In contrast, the PWC method introduces less noise, but it is less adaptable to cases of large variations of tropospheric delay. To address the problem of how to choose the optimal TD parameter estimation method, this paper investigates the variation patterns of international GNSS service zenith tropospheric delay (IGS ZTD) products and proposes a combined strategy model for TD parameter estimation. Firstly, this paper avoids the day-boundary jumps problem of IGS ZTD products by grouping based on single-day data. Secondly, this paper introduces discrete point areas (DPAs) to measure the magnitude of the ZTD values and uses comprehensive indicators to reflect the variation of ZTD. Next, based on the Köppen-Geiger climate classification, this study selected five different climate classifications with a total of 20 IGS stations as experimental data. The data assessed span from day of year (DOY) 001 to DOY 365 in 2022. This paper then applied 26 different parameter estimation strategies for static precise point positioning (PPP) data processing, and the parameter estimation strategies that were used include the RW and PWC (with the piece-wise constant ranging from twenty minutes to five hundred minutes at twenty-minute intervals). Finally, ZTD and positioning results were obtained using various parameter estimation methods, and a combined strategy model was established. We selected five different climate classifications of IGS stations as validation data and designed three sets of comparative experiments: RW, PWC120, and the combined strategy model, to verify the effectiveness of the combined strategy model. The experimental results revealed that: RW and the combined strategy model have a comparable ZTD accuracy and both are superior to PWC120. The combined strategy model improves the positioning accuracy in the U direction compared to RW and PWC120. In arid (B) and polar (E) regions with a small variation of TD, the PWC120 strategy displayed a better positioning accuracy than the RW strategy; in equatorial (A) and warm-temperate (C) regions, where there are large variations of TD, the RW strategy exhibited a better positioning accuracy than the PWC120 strategy. The combined strategy model can flexibly select the optimal parameter estimation method according to the comprehensive indicator while ensuring ZTD estimation accuracy; it enhances positioning accuracy.

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Assessment of GNSS zenith tropospheric delay responses to atmospheric variables derived from ERA5 data over Nigeria

Cited by 5 publications

References 43 publications

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations

Tropospheric Delay Parameter Estimation Strategy in BDS Precise Point Positioning

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