Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data

Selbesoglu, Mahmut Oguz

doi:10.1016/j.jestch.2019.11.006

Cited by 27 publications

(14 citation statements)

References 26 publications

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“…For example, the tropospheric water vapor content is a derivative parameter from zenith wet delay (ZWD) and can be calculated as a function of the refractive index, depending on temperature, pressure and relative humidity [25][26][27]. Selbesoglu [24] claims that the troposphere affects GNSS signals due to the fast variability of the refraction index. Determination of the wet delay is more difficult than the hydrostatic delay due to much faster changes in the water vapor in the troposphere.…”

Section: Methodsmentioning

confidence: 99%

“…In this article, the authors attempt to quantify the effect of solar activity (expressed by sunspots) on satellite navigation signal integrity, using fuzzy logic. However, in order to look at the problem more broadly, attention was also paid to other atmospheric (tropospheric) conditions, and in this case, unlike other research in the literature [24][25][26][27], it was verified whether, against the background of cloudiness, precipitation, humidity, pressure and temperature, solar activity affects the integrity to the greatest extent. Moreover, research usually deals with accuracy rather than signal integrity.…”

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confidence: 97%

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On the Correlation of Solar Activity and Troposphere on the GNSS/EGNOS Integrity. Fuzzy Logic Approach

Krzykowska-Piotrowska

Dudek

Wielgosz

et al. 2021

Energies

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There is a noticeable number of studies carried out on both the operational parameters of Global Navigation Satellite System (GNSS) and the satellite signal itself. Researchers look for, inter alia, proven sources of errors affecting the integrity of the satellite signal because this parameter determines the approval of the system’s operational use. It also seems of key importance that the atmospheric conditions, in any area of satellite signal usage, should not be underestimated due to their extensive impact. As the ionospheric refraction seriously limits the operational use of the satellite navigation signal, in this article, the authors attempted to quantify the effect of solar activity (expressed by sunspots) on the signal integrity using fuzzy logic. Fuzzy reasoning is used when information is inaccurate or incomplete and necessitates making decisions under conditions of uncertainty. Thanks to fuzzy sets, there are no obstacles to characterize the degree of intensity of a given phenomenon. In order to look at the problem more broadly, attention was also paid to the tropospheric conditions, and it was verified whether, against the background of cloudiness, precipitation, humidity, pressure and temperature, solar activity affects the integrity to the greatest extent. The integrity measurements from the EGNOS system (PRN120 and PRN126) collected at the monitoring station in Warsaw, Poland in 2014 were used.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 97%

On the Correlation of Solar Activity and Troposphere on the GNSS/EGNOS Integrity. Fuzzy Logic Approach

Krzykowska-Piotrowska

Dudek

Wielgosz

et al. 2021

Energies

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“…Calculate α u (n). e first is the error between the real output and the predicted value [22], and then calculate α u (n) of each node of the error to the result value:…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Artificial Neural Network Model in Spatial Analysis of Geographic Information System

2021

Mobile Information Systems

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In the past two decades, the computer technology industry has developed rapidly, and the geological prospecting industry is also undergoing a computerized and electronic revolution. The application technology of new geological information systems is gradually adding us to the spatial information system of geological prospecting projects. In order to deeply study the current situation of the artificial neural network model in the spatial analysis of our country’s geographic information system, this paper uses the traditional classification analysis method; database analysis and neural network analysis method of compensating samples were collected, an artificial model of the network is established, and the algorithm is simplified. And a neural network model is created. In the research of A and B counties’ geographic information system, using a new network model, 61 geological disasters were found in County A, of which 47 were landslides, 4 collapses, and 10 unstable slopes. There were 19 geographical disasters in County B, including 9 unstable slopes, 6 landslides and 4 collapses. In terms of geographic prediction combined with the network model, the comparison with the actual situation shows that the geographical distribution is 99.7% in the geographical and geological disaster-prone areas, and the geographical distribution is less in the nonprone areas, with a proportion of 0.3%. Geological disaster-prone areas of low points accounted for 76.9%, and the number of disaster-affected points in the low-prone areas accounted for 22.8%. The geographical and geological grades divided by the evaluation model are basically consistent with the actual grades, which can meet the needs of geographic evaluation. It is basically realized that starting from the model’s geographic information system, a more comprehensive and practical artificial neural network model is designed.

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“…In climate science, deep learning has recently been applied to a number of different problems, including microphysics (Seifert and Rasp, 2020), radiative transfer (Min et al, 2020), convection (O'Gorman and Dwyer, 2018), forecasting (Roesch and Günther, 2019;Selbesoglu, 2019;Weyn et al, 2019), and empirical-statistical downscaling (Baño-Medina et al, 2020). For example, Yuval et al (2021) have applied deep learning for parametrization of subgrid scale atmospheric processes like convection.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks

2021

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Numerical weather and climate simulations nowadays produce terabytes of data, and the data volume continues to increase rapidly since an increase in resolution greatly benefits the simulation of weather and climate. In practice, however, data is often available at lower resolution only, for which there are many practical reasons, such as data coarsening to meet memory constraints, limited computational resources, favoring multiple low-resolution ensemble simulations over few high-resolution simulations, as well as limits of sensing instruments in observations. In order to enable a more insightful analysis, we investigate the capabilities of neural networks to reconstruct high-resolution data from given low-resolution simulations. For this, we phrase the data reconstruction as a super-resolution problem from multiple data sources, tailored toward meteorological and climatological data. We therefore investigate supervised machine learning using multiple deep convolutional neural network architectures to test the limits of data reconstruction for various spatial and temporal resolutions, low-frequent and high-frequent input data, and the generalization to numerical and observed data. Once such downscaling networks are trained, they serve two purposes: First, legacy low-resolution simulations can be downscaled to reconstruct high-resolution detail. Second, past observations that have been taken at lower resolutions can be increased to higher resolutions, opening new analysis possibilities. For the downscaling of high-frequent fields like precipitation, we show that error-predicting networks are far less suitable than deconvolutional neural networks due to the poor learning performance. We demonstrate that deep convolutional downscaling has the potential to become a building block of modern weather and climate analysis in both research and operational forecasting, and show that the ideal choice of the network architecture depends on the type of data to predict, i.e., there is no single best architecture for all variables.

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Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data

Cited by 27 publications

References 26 publications

On the Correlation of Solar Activity and Troposphere on the GNSS/EGNOS Integrity. Fuzzy Logic Approach

On the Correlation of Solar Activity and Troposphere on the GNSS/EGNOS Integrity. Fuzzy Logic Approach

Artificial Neural Network Model in Spatial Analysis of Geographic Information System

Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks

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