A new ZTD model based on permanent ground-based GNSS-ZTD data

Ding, Maohua; Hu, Wusheng; Jin, Xiaodong; Yu, Longfei

doi:10.1179/1752270615y.0000000034

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…Although the height factor has been considered by the GPT3 model when calculating the corresponding P and T at GNSS stations [19], the height-related residual still existed for regions, especially in China with large height differences. Similarly, the developed ZTD models without considering the height factor, such as the ISAAS model [44], the GHOP model [22], and the AFRC-TROP model [23], are significantly affected by height. Therefore, the relationship between ZTD periodical residual and height is further explored in this section.…”

Section: Relationship Analysis Between Ztd Periodical Residual and He...mentioning

confidence: 99%

High-Precision ZTD Model of Altitude-Related Correction

Zhao

Jing

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Relationship Analysis Between Ztd Periodical Residual and He...mentioning

confidence: 99%

High-Precision ZTD Model of Altitude-Related Correction

Zhao

Jing

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Zheng et al, (2015) improved the ZTD prediction accuracy by combining the BPNN and Hopfield models, their accuracy is approximately 4 mm. Ding et al, (2016) employed IGS ZTD data study the residuals in the Saastamoinen model, they proposed a ZTD forecast model based on ground meteorological parameters and BPNN modeling, the RMSE was approximately 20.4 mm in Russia. Yang et al, (2017) employed the UNB3m model to calculate the temperature, air pressure, and relative humidity of a local area and established a "UNB3m+GA-BP" regional tropospheric delay model based on the BPNN, verified by GNSS data in Hong Kong, the accuracy is approximately 1.1 cm.…”

Section: Introductionmentioning

confidence: 99%

A New Global ZTD Forecast Model Based on Improved LSTM Neural Network

He,

Yao,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Zenith tropospheric delay (ZTD), consisting of zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD), is a significant contributor to errors in precise positioning using the Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) and Real Time Kinematic (RTK) techniques. Accurate and timely predictions of ZTD on a global scale are crucial for enhancing GNSS positioning accuracy and expediting convergence. This study proposes an innovative global tropospheric prediction model that leverages Long Short-Term Memory (LSTM) neural networks. Aiming to achieve both high precision and long-term prediction capability for ZTD. The experimental data utilized were sourced from the VMF3_OP (Vienna Mapping Functions 3-Optimized) Zenith Total Delay (ZTD) dataset. This study delves further into the analysis of ZTD residuals by extracting periodic signals. The ZTD residuals were then utilized to train a modified long short-term memory (LSTM) neural network model, enabling the prediction of global residuals. The final ZTD predictions were obtained by combining the modified LSTM ZTD residual forecast component with the ZTD periodic component. Our results demonstrate that the average root-mean-square error (RMSE) of the modified LSTM-ZTD model in 2020 was 1.44 cm. Additionally, the average RMSE of the forecasted ZTD during spring, summer, autumn, and winter was found to be 1.43 cm, 1.47 cm, 1.56 cm, and 1.36 cm, respectively. Through the integration of the LSTM neural network and the ZTD periodic signal extracted using a physical algorithm, this work has successfully enhanced the accuracy and time span of ZTD forecasts on a global scale.

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“…The advantage of neural network is that they are best suited to solving the problems that are the most difficult to solve by traditional computational methods 30 , Neural networks can learn from examples (past data) recognize a hidden pattern in historical observations and use them to forecast future values 31 . In addition 32 , proposed a multilayer feedforward neural network (the NN) model for weighted mean temperature of atmospheric water vapor predicting, and the result shows the good performance of NN model on global scale 33 . proposed a new ZTD model based on a back propagation neural network, and the ZTD prediction accuracy has been improved by more than 12.4%.…”

Section: Introductionmentioning

confidence: 99%

Short-term rainfall forecast model based on the improved BP–NN algorithm

Liu

Zhao

Yao

et al. 2019

Sci Rep

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The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP–NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP–NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010–2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP–NN algorithm.

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A new ZTD model based on permanent ground-based GNSS-ZTD data

Cited by 16 publications

References 14 publications

High-Precision ZTD Model of Altitude-Related Correction

High-Precision ZTD Model of Altitude-Related Correction

A New Global ZTD Forecast Model Based on Improved LSTM Neural Network

Short-term rainfall forecast model based on the improved BP–NN algorithm

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