Analyzing Different Parameterization Methods in GNSS Tomography Using the COST Benchmark Dataset

Adavi, Zohreh; Rohm, Witold; Weber, Robert L.

doi:10.1109/jstars.2020.3027909

Cited by 6 publications

(2 citation statements)

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“…This study recommended using stacked data over a 30 min window (increasing the number of available rays) and pseudo-slant observations in case studies and nowcasting. Regarding parametrization, another study compared some methods using the European Cooperation in Science and Technology (COST) data-set (central Europe) [40]. Among them, ray-tracing was found to be more adequate than straight-line methods.…”

Section: Tomographymentioning

confidence: 99%

Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics

Vaquero-Martínez

Antón

2021

Remote Sensing

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After 30 years since the beginning of the Global Positioning System (GPS), or, more generally, Global Navigation Satellite System (GNSS) meteorology, this technique has proven to be a reliable method for retrieving atmospheric water vapor; it is low-cost, weather independent, with high temporal resolution and is highly accurate and precise. GNSS ground-based networks are becoming denser, and the first stations installed have now quite long time-series that allow the study of the temporal features of water vapor and its relevant role inside the climate system. In this review, the different GNSS methodologies to retrieve atmospheric water vapor content re-examined, such as tomography, conversion of GNSS tropospheric delay to water vapor estimates, analyses of errors, and combinations of GNSS with other sources to enhance water vapor information. Moreover, the use of these data in different kinds of studies is discussed. For instance, the GNSS technique is commonly used as a reference tool for validating other water vapor products (e.g., radiosounding, radiometers onboard satellite platforms or ground-based instruments). Additionally, GNSS retrievals are largely used in order to determine the high spatio-temporal variability and long-term trends of atmospheric water vapor or in models with the goal of determining its notable influence on the climate system (e.g., assimilation in numerical prediction, as input to radiative transfer models, study of circulation patterns, etc.).

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Section: Tomographymentioning

confidence: 99%

Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics

Vaquero-Martínez

Antón

2021

Remote Sensing

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“…There are still some drawbacks to these techniques, such as their high cost and poor spatial-temporal resolution, even though they have many advantages. These limitations can be mitigated by GNSS due to its continuous scanning of the troposphere at a low cost with a higher spatial-temporal resolution in all-weather conditions [1,[3][4][5]. Thereby, GNSS observations can be used to estimate PWV with long-term stability, high accuracy, and high spatiotemporal resolution due to the development of GNSS technology and the increasing number of permanently tracking GNSS receivers [1,[6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

Adavi,

Ghassemi,

Weber

et al. 2023

Remote Sensing

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Water vapour plays a key role in long-term climate studies and short-term weather forecasting. Therefore, to understand atmospheric variations, it is crucial to observe water vapour and its spatial distribution. In the current era, Global Navigation Satellite Systems (GNSS) are widely used to monitor this critical atmospheric component because GNSS signals pass through the atmosphere, allowing us to estimate water vapour at various locations and times. The amount of precipitable water vapour (PWV) is one of the most fascinating quantities, which provides meteorologists and climate scientists with valuable information. However, calculating PWV accurately from processing GNSS observations usually requires the input of further observed meteorological parameters with adequate quality and latency. To bypass this problem, hourly PWVs without meteorological parameters are computed using the Random Forest and Artificial Neural Network algorithms in this research. The first step towards this objective is establishing a regional weighted mean temperature model for Austria. To achieve this, measurements of radiosondes launched from different locations in Austria are employed. The results indicate that Random Forest is the most accurate method compared to regression (linear and polynomial), Artificial Neural Network, and empirical methods. PWV models are then developed using data from 39 GNSS stations that cover Austria’s entire territory. The models are afterwards tested under different atmospheric conditions with four radiosonde stations. Based on the obtained results, the Artificial Neural Network model with a single hidden layer slightly outperforms other investigated models, with only a 5% difference in mean absolute error. As a result, the hourly PWV can be estimated without relying on measured meteorological parameters with an average mean absolute error of less than 2.5 mm in Austria.

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Pre-analysis of GNSS tomography solution using the concept of spread of model resolution matrix

Adavi

Weber

Rohm

2022

J Geod

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GNSS tropospheric tomography is one of the applications of the Global Navigation Satellite Systems (GNSS) signals which attracts more and more interest in the field of meteorology. This method can reconstruct the water vapour of the atmosphere, which has a considerable effect on weather forecasting and early warning systems of severe weather. In GNSS tomography, traditionally, a regular spaced 3D grid stretches from the GNSS network to the effective height of the troposphere in the area of interest. Therefore, this method can offer a permanent monitoring service for water vapour and wet refractivity fields at a low cost and a reasonable spatial resolution compared to conventional observations, like radiosonde and radio occultation profiles. Nevertheless, the quality of the reconstructed field is still one of the challenges in the GNSS tomography. In this research, we propose the concept of spread as a mathematical tool to provide a quality measure without using the reference field and calculating statistical measures like RMSE and Bias. Thereby, two synthetic and one real datasets (part of Germany and Czechia) covering overlapping periods between 29 May and 14 Jun of the year 2013 (DoY 149–165; DoY 160–165; DoY 160–165, 2013) have been tested to investigate the proposed method. According to the obtained results, the proposed tool shows a strong correlation (up to 0.81 for synthetic and 0.72 for real observations) with the standard deviation of the reconstructed wet refractivity with respect to the radiosonde profile reference. The correlation between spread and the Bias of the retrieved wet refractivity field is also significant. However, there is no clear picture depending on the applied spread computation models. Therefore, the spread of the resolution matrix can be used as a proxy for the accuracy of the tomography reconstruction field based on the quality of the observations, the initial field, and the design matrix.

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Analyzing Different Parameterization Methods in GNSS Tomography Using the COST Benchmark Dataset

Cited by 6 publications

References 50 publications

Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics

Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

Pre-analysis of GNSS tomography solution using the concept of spread of model resolution matrix

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