Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data
Estimation of tropospheric wet delay is of great importance for real-time weather forecasting applications. In the last decade, based on troposphere wet delays obtained from Global Navigation Satellite System observations, high temporal and spatial resolution water vapor data can be produced for rel...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098619316003 |
| _version_ | 1818148605359292416 |
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| author | Mahmut Oguz Selbesoglu |
| author_facet | Mahmut Oguz Selbesoglu |
| author_sort | Mahmut Oguz Selbesoglu |
| collection | DOAJ |
| description | Estimation of tropospheric wet delay is of great importance for real-time weather forecasting applications. In the last decade, based on troposphere wet delays obtained from Global Navigation Satellite System observations, high temporal and spatial resolution water vapor data can be produced for reliable and accurate weather forecasting. The main objective of this study is to investigate the accuracy of tropospheric wet delay prediction based on artificial neural network technology by the integration of Global Navigation Satellite System and meteorological data from in-situ observations of The New Austrian Meteorological Measuring Network. In the study, artificial neural network model was used to predict the wet troposphere delay up to six hour. Predicted zenith wet delay values were compared with the values estimated from Global Navigation Satellite System observations for validation. The predictions were carried out during humid (August) and dry (December) periods on two reference stations belonging to Echtzeit Positionierung Austria GNSS Network of Austria. The root mean square error of zenith wet delay prediction based on newly designed artificial neural network Model was found 1.5 cm for up to six hours. |
| first_indexed | 2024-12-11T12:53:48Z |
| format | Article |
| id | doaj.art-a5bda0d23ec14f9f8024b27e46e5d303 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-12-11T12:53:48Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-a5bda0d23ec14f9f8024b27e46e5d3032022-12-22T01:06:38ZengElsevierEngineering Science and Technology, an International Journal2215-09862020-10-01235967972Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS dataMahmut Oguz Selbesoglu0Yildiz Technical University, Faculty of Civil Engineering, Department of Geomatic Engineering, 34220 Istanbul, TurkeyEstimation of tropospheric wet delay is of great importance for real-time weather forecasting applications. In the last decade, based on troposphere wet delays obtained from Global Navigation Satellite System observations, high temporal and spatial resolution water vapor data can be produced for reliable and accurate weather forecasting. The main objective of this study is to investigate the accuracy of tropospheric wet delay prediction based on artificial neural network technology by the integration of Global Navigation Satellite System and meteorological data from in-situ observations of The New Austrian Meteorological Measuring Network. In the study, artificial neural network model was used to predict the wet troposphere delay up to six hour. Predicted zenith wet delay values were compared with the values estimated from Global Navigation Satellite System observations for validation. The predictions were carried out during humid (August) and dry (December) periods on two reference stations belonging to Echtzeit Positionierung Austria GNSS Network of Austria. The root mean square error of zenith wet delay prediction based on newly designed artificial neural network Model was found 1.5 cm for up to six hours.http://www.sciencedirect.com/science/article/pii/S2215098619316003GNSS meteorologyWeather forecastArtificial neural networkClimateTroposphere wet delay |
| spellingShingle | Mahmut Oguz Selbesoglu Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data Engineering Science and Technology, an International Journal GNSS meteorology Weather forecast Artificial neural network Climate Troposphere wet delay |
| title | Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data |
| title_full | Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data |
| title_fullStr | Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data |
| title_full_unstemmed | Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data |
| title_short | Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data |
| title_sort | prediction of tropospheric wet delay by an artificial neural network model based on meteorological and gnss data |
| topic | GNSS meteorology Weather forecast Artificial neural network Climate Troposphere wet delay |
| url | http://www.sciencedirect.com/science/article/pii/S2215098619316003 |
| work_keys_str_mv | AT mahmutoguzselbesoglu predictionoftroposphericwetdelaybyanartificialneuralnetworkmodelbasedonmeteorologicalandgnssdata |