Global model of zenith tropospheric delay proposed based on EOF analysis

Sun, Langlang; Chen, Peng; Wei, Erhu; Li, Qinzheng

doi:10.1016/j.asr.2017.03.045

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…Hopfield model and Saastamoinen model can estimate ZTD based on surface meteorology [7]- [9]. In Hopfield model, refractivity at any height can be given by…”

Section: Methodology and Models A Conventional Methodsmentioning

confidence: 99%

Estimating Zenith Tropospheric Delay Based on GPT2w Model

2019

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To precisely and conveniently estimate zenith tropospheric delay (ZTD), troposphere refractivity is deduced through global pressure and temperature 2w (GPT2w) model and surface meteorology devices, ZTD is estimated based on integration of refractivity in the zenith direction. When surface meteorological measurements are absent, all parameters including surface meteorology and lapse rate are estimated by GPT2w. Validation is carried out using observed data in the year 2012 at 8 international GNSS service (IGS) stations. Results indicate that when surface meteorological parameters are available, the annual accuracy is improved by 27% than Saastamoinen model. Meanwhile, annual bias of the other scheme is decreased by 21% than the GPT2w+Saastamoinen model. For most stations, the bias shows seasonal characteristics, high elevation, high longitude and low latitude can bring less bias.INDEX TERMS Zenith tropospheric delay (ZTD), GPT2w model, refractivity, meteorological parameters.

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“…Hopfield model and Saastamoinen model can estimate ZTD based on surface meteorology [7]- [9]. In Hopfield model, refractivity at any height can be given by…”

Section: Methodology and Models A Conventional Methodsmentioning

confidence: 99%

Estimating Zenith Tropospheric Delay Based on GPT2w Model

2019

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“…VMF3 is the successor of VMF1 realized on both 1˚ and 5˚ global grids (Landskron and Böhm, 2018). This study utilized the 1˚ gridded VMF3-ZTD products as a reference for evaluating the prediction models ZTD following (Yao et al, 2016;Sun et al, 2017) and also owing to the fact that the CORS employed in this study are not International GNSS Service (IGS). The precision and accuracy of the VMF-ZTD data have been investigated and validated against the IGS-ZTDs by (Yao et al, 2017(Yao et al, , 2018 to be accurate enough for tropospheric delay mitigation, model development and validation.…”

Section: Vmf Ztd Datamentioning

confidence: 99%

Comparative evaluation and analysis of different tropospheric delay models in Ghana

Osah¹,

Acheampong²,

Dadzie³

et al. 2022

SA J of Geomatics

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Tropospheric delay prediction models have become increasingly important in Global Navigation Satellite System (GNSS) as they play a critical role in GNSS positioning applications. Due to the different atmospheric conditions over the earth regions, tropospheric effect on GNSS signals also differs, influencing the performance of these prediction models. Thus, the choice of a particular prediction model can significantly degrade the positioning accuracy especially when the model does not suit the user’s environs. Therefore, a performance assessment of existing prediction models in various regions for a suitable one is very imperative. This paper evaluates and analyses seven commonly used tropospheric delay models in Ghana in terms of performances in Zenith Tropospheric Delay (ZTD) estimation and baseline positional accuracies using data from six selected Continuously Operating Reference Stations (CORS). The 1˚x1˚ gridded Vienna Mapping Functions 3 (VMF3) ZTD product and coordinates solutions from the CSRS-PPP positioning service were respectively used as references. The results show that the Black model performed better in estimating the ZTD, followed by Askne and Nordius model. The Saastamoinen, Marini and Murray, Niell, Goads and Goodman and Hopfield models respectively performed poorly. However, the result of the baseline solutions did not show much variation in the coordinate difference provided by the use of the prediction models, nonetheless, the Black and Askne and Nordius models continue to dominate the other models. Of all the models evaluated, either Black or Askne and Nordius model is recommended for use to mitigate the ZTD in the study area, however, the choice of the Black model will be more desirable.

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“…The troposphere is a non-dispersive medium, and the tropospheric delay is frequency-independent. This delay cannot be weakened by dual-frequency or multi-frequency combinations [2,3]. The tropospheric delay is related to the observed satellite elevation angle, and the Zenith Tropospheric Delay (ZTD) is usually mapped to the slant path of the observed satellite elevation angle by a mapping function.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

Wu,

Huang,

Feng

et al. 2024

Atmosphere

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The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm.

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Global model of zenith tropospheric delay proposed based on EOF analysis

Cited by 12 publications

References 21 publications

Estimating Zenith Tropospheric Delay Based on GPT2w Model

Estimating Zenith Tropospheric Delay Based on GPT2w Model

Comparative evaluation and analysis of different tropospheric delay models in Ghana

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

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