A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network

Yao, Yibin; Liu, Chen; Xu, Chaoqian

doi:10.3390/rs12142306

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…However, as shown in Fig. 7, the Informer-WV model achieves a favorable prediction accuracy (an MAE below 1 g/m³ and an RMSE below 1.2 g/m³) for the vast majority of the study periods, which suggests that the overall accuracy of the initial tomography values predicted by the model is superior to that of the tomography results in previously published papers (Dong and Jin 2018; Yao et al 2020a;Zhao et al 2020;Trzcina et al 2023). First, near-real-time water vapor density inversion results are obtained by simulating the year-round data using two separate tomography methods with a 30-minute tomography period.…”

Section: 2-10 Km: the Trad Methods Resultsmentioning

confidence: 68%

“…Several researchers have proposed diverse solutions to address the challenges of low GNSS signal ray availability and prevalence of voxels without ray penetration. These solutions include considering only voxels with ray passages (Yao et al 2020a), incorporating models that adaptively include side-passing signals into the observation equation (Zhao et al 2020) and tting water vapor density thresholds for determining seasonal stratigraphic layer top heights (Long et al 2023). Additionally, real-time 3D water vapor density information is provided as initial tomography values, including traditional models based on exponential and linear least squares models and advanced neural network models (Liu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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Optimized Approach for Near-Real-Time Three-Dimensional Water Vapor in the GNSS Based on the Informer Model

Yixin,

Pengfei,

Shirong

et al. 2024

Preprint

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The integration of near-real-time three-dimensional (3D) water vapor data into numerical weather prediction is crucial for monitoring and forecasting extreme weather events but faces various challenges. We focus on reconstructing the 3D water vapor field using Global Navigation Satellite System (GNSS) water vapor tomography techniques, emphasizing two primary concerns: achieving high-precision initial 3D water vapor values and effectively partitioning the vertical tomography grid. We introduce a novel real-time, high-precision water vapor prediction model, namely, the Informer-WV model, based on the Informer framework, whose predictions serve as the initial values for tomography. Furthermore, we propose an innovative method for nonuniform vertical delineation of the tomography grid in which the upper boundary height of the 3D tomography grid is determined by the vertical prediction accuracy of the model. For practical application purposes, Hong Kong, China, was chosen as the study area. The Informer-WV model, utilizing ERA5 reanalysis data, successfully predicted the regional water vapor density for 2022. The model demonstrated a remarkable prediction accuracy, with an annual root mean square error (RMSE) better than 0.80 g/m³ compared to the actual ERA5 values. Building on this high-precision prediction, we adjusted the upper boundary altitude of the tomography grid to 5.2 km, specifically for Hong Kong. By benchmarking against radiosonde-derived water vapor density data, we analyzed the near-real-time tomography inversion results for the two weakest prediction periods of the model. The RMSE of the water vapor inversion values derived from our optimized method was reduced to 1.26 g/m³. This approach not only improved the accuracy by 19% relative to the initial predictions but also significantly outperformed the traditional tomography method.

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Section: 2-10 Km: the Trad Methods Resultsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Optimized Approach for Near-Real-Time Three-Dimensional Water Vapor in the GNSS Based on the Informer Model

Yixin,

Pengfei,

Shirong

et al. 2024

Preprint

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show abstract

“…The RO excess path of ≈ 100 m in the lowermost section of the troposphere (below 5 km) is going to traverse the tomography model that spans at least 300 km, with at least 5 to 10 voxels (horizontal resolution). A large number of empty voxels can significantly increase the RMSE of the tomography solution (Yao et al 2020). Filling them with additional RO simulated data can work similarly to, e.g., adding virtual observation (Zhang et al 2021) and decreasing tomography uncertainties.…”

Section: Excess Phase Data Validationmentioning

confidence: 99%

GNSS signal ray-tracing algorithm for the simulation of satellite-to-satellite excess phase in the neutral atmosphere

Cegla,

Rohm,

Moeller

et al. 2024

J Geod

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Traditionally, GNSS space-based and ground-based estimates of tropospheric conditions are performed separately. It leads to limitations in the horizontal (e.g., a single space-based radio occultation profile covers a 300 km slice of the troposphere) and vertical resolution (e.g., ground-based estimates of troposphere conditions have spacing equal to stations’ distribution) of the tropospheric products. The first stage to achieve an integrated model is to create an effective 3D ray-tracing algorithm for the satellite-to-satellite (radio occultation) path reconstruction. We verify the consistency of the simulated data with the RO observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) Data Analysis and Archive Center (CDAAC) in terms of excess phase and bending angle. The results show that our solution provides an effective RO excess phase, with a relative error varying from 35% at the height of 25–30 km (1.0–1.5 m) to 0.5% at heights 5–10 km (0.1–1 m) and 14 to 2% at heights below 5 km (2–14 m). The bending angle retrieval on simulated data attained for high-resolution ray-tracing, bias lower than 2% with respect to the observed bending angle. The optimal solution takes about 1 s for one transmitter–receiver pair with a tangent point below 5 km altitude. The high-resolution processing solution takes 3 times longer.

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“…According to a previous study [27], the optimized voxel scheme was applied in GNSS-WV tomography. The optimized voxels refer to ray-passed through voxels, i.e., in the tomography processes, only ray-passed through voxels participate to establish the tomography equation.…”

Section: Grid Schemementioning

confidence: 99%

A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

Yao

Liu

et al. 2020

Remote Sensing

Self Cite

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Global navigation satellite system (GNSS) tomography can effectively sense the three-dimensional structure of tropospheric water vapor (WV) using the GNSS observations. Numerous studies have utilized a tomographic window to include more epochs of observations, which significantly increases the number of valid signals. However, considering the tomography grid limits, a massive number of valid signals inevitably exhibits linear dependence. This dependence makes it impossible to improve the rank score of the tomography coefficient matrix by blindly introducing a large number of valid rays. Furthermore, excessive valid signals may lead to a high condition number in the coefficient matrix (ill-condition problem), which causes unstable results using the GNSS-WV tomography. Considering these problems, we proposed an improved tomographic approach, which applies a refined tomographic window. It differs from the general tomographic window in that the window is refined to traverse the valid signals available 15 min before and after the target epoch while retaining only the linearly independent parts (characteristic signal). Compared to the conventional method, the proposed method can filter the characteristic signal, which increases the rank score of the coefficient matrix and improves the stability of the tomography model. In this paper, we used GNSS observations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) to validate the performance of the proposed method over the day-of-year (DOY) periods of 130–132, 2019 and 146–148, 2019. The numerical results showed that, by using a refined tomographic window, the proposed method obtained superior WV products in comparison with that of the conventional method.

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A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network

Cited by 14 publications

References 38 publications

Optimized Approach for Near-Real-Time Three-Dimensional Water Vapor in the GNSS Based on the Informer Model

Optimized Approach for Near-Real-Time Three-Dimensional Water Vapor in the GNSS Based on the Informer Model

GNSS signal ray-tracing algorithm for the simulation of satellite-to-satellite excess phase in the neutral atmosphere

A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

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