A proposed neural network model for obtaining precipitable water vapor
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Hadeer Al-Eshmawy
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Mohamed A. Abdelfatah
Abstract
The atmospheric Precipitable water vapor (PWV) is a variable key for weather forecasting and climate change. It is a considerable component of the atmosphere, influencing numerous atmospheric processes, and having physical characteristics. It can be measured directly using radiosonde stations (RS), which are not always accessible and difficult to measure with acceptable spatial and time precision. This study uses the artificial neural network (ANN) application to propose a simple model based on RS data to estimate PWV from surface metrological data. Ten RS stations were used to develop the new model for eight and a half years. In addition, two and a half years of data were used to validate the developed model. The study period is based on the data accessible between 2010 and 2020. The new model needs to collect (vapor pressure, temperature, latitude, longitude, height, day of year, and relative humidity) as input parameters in ANN to predict the PWV. The ANN model validations were based on the root mean square (RMS), correlation coefficient (CC), and T-test. According to the results, the proposed ANN can accurately predict the PWV over Egypt. The results of the new ANN model and eight other empirical models (Saastamoinen, Askne and Nordius, Okulov et al., Maghrabi et al., Phokate., Falaiye et al. (A&B), Qian et al. and ERA 5) are compared in addition, the new PWV model can achieve the best performance with RMS of 0.21 mm. The new model can serve as a will be of practical utility with a high degree of precision in PWV estimation.
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Research ethics: There was no content theft in this study.
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Author contributions: The author contributed to this study by developing a formula for calculating precipitable water vapor.
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Competing interest: There is no competing of interest.
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Research funding: There is no external funding.
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Data availability: Data is available on atmospheric soundings http://weather.uwyo.edu/upperair/sounding.html.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Review
- A proposed neural network model for obtaining precipitable water vapor
- Original Research Articles
- Assessment of GNSS observations and positioning performance from non-flagship Android smartphones
- Dynamic mode decomposition and bivariate autoregressive short-term prediction of Earth rotation parameters
- Comparison of selected reliability optimization methods in application to the second order design of geodetic network
- Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
- Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
- Keypoint-based registration of TLS point clouds using a statistical matching approach
- PPP_Mansoura: an open-source software for multi-constellation GNSS processing
- Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017
- Analysis of differential code biases for GPS receivers over the Indian region
- A machine-learning approach to estimate satellite-based position errors
- Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)
Artikel in diesem Heft
- Frontmatter
- Review
- A proposed neural network model for obtaining precipitable water vapor
- Original Research Articles
- Assessment of GNSS observations and positioning performance from non-flagship Android smartphones
- Dynamic mode decomposition and bivariate autoregressive short-term prediction of Earth rotation parameters
- Comparison of selected reliability optimization methods in application to the second order design of geodetic network
- Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
- Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
- Keypoint-based registration of TLS point clouds using a statistical matching approach
- PPP_Mansoura: an open-source software for multi-constellation GNSS processing
- Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017
- Analysis of differential code biases for GPS receivers over the Indian region
- A machine-learning approach to estimate satellite-based position errors
- Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)
