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

Araştırma sonucu: ???type-name???Makalebilirkişi

32 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)967-972
Sayfa sayısı6
DergiEngineering Science and Technology, an International Journal
Hacim23
Basın numarası5
DOI'lar
Yayın durumuYayınlandı - Eki 2020
Harici olarak yayınlandıEvet

Bibliyografik not

Publisher Copyright:
© 2019 Karabuk University

Finansman

I would like to thank Robert Weber and Johannes Böhm from the Geodesy and Geoinformation Department of the Vienna University of Technology for providing the data of TAWES and EPOSA Network.

FinansörlerFinansör numarası
Geodesy and Geoinformation Department of the Vienna University of Technology

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