Kriging Interpolation in Modelling Tropospheric Wet Delay
This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to...
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doaj-9be2ec487814443781b31794fb66b43f2020-11-25T03:57:44ZengMDPI AGAtmosphere2073-44332020-10-01111125112510.3390/atmos11101125Kriging Interpolation in Modelling Tropospheric Wet DelayHongyang Ma0Qile Zhao1Sandra Verhagen2Dimitrios Psychas3Han Dun4GNSS Research Center, Wuhan University, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaDepartment of Geoscience and Remote Sensing, Delft University of Technology, 2600 AA Delft, The NetherlandsDepartment of Geoscience and Remote Sensing, Delft University of Technology, 2600 AA Delft, The NetherlandsDepartment of Geoscience and Remote Sensing, Delft University of Technology, 2600 AA Delft, The NetherlandsThis contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to evaluate the performances of the Kriging interpolation, a sparse network with 8 stations and a dense network with 19 stations from continuously operating reference stations (CORS) of the Netherlands are selected as the reference. In addition, other 15 CORS stations are selected as users, which are divided into three blocks: 5 stations located approximately in the center of the networks, 5 stations on the edge of the networks and 5 stations outside the networks. The zenith tropospheric wet delays are estimated at the network and user stations through the ionosphere-free positioning model; meanwhile, the predicted wet delays at the user stations are generated by the Kriging interpolation in the use of the tropospheric estimations at the network. The root mean square errors (RMSE) are calculated by comparing the predicted wet delays and estimated wet delays at the same user station. The results show that RMSEs of the stations inside the network are at a sub-centimeter level with an average value of 0.74 cm in the sparse network and 0.69 cm in the dense network. The stations on edge and outside the network can also achieve 1-cm level accuracy, which overcomes the limitation that accurate interpolations can only be attained inside the network. This contribution also presents an insignificant improvement of the prediction accuracy from the sparse network to the dense network over 1-year’s data processing and a seasonal effect on the tropospheric wet delay predictions.https://www.mdpi.com/2073-4433/11/10/1125GNSStropospheretropospheric delaykriging interpolationprecise point positioning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongyang Ma Qile Zhao Sandra Verhagen Dimitrios Psychas Han Dun |
spellingShingle |
Hongyang Ma Qile Zhao Sandra Verhagen Dimitrios Psychas Han Dun Kriging Interpolation in Modelling Tropospheric Wet Delay Atmosphere GNSS troposphere tropospheric delay kriging interpolation precise point positioning |
author_facet |
Hongyang Ma Qile Zhao Sandra Verhagen Dimitrios Psychas Han Dun |
author_sort |
Hongyang Ma |
title |
Kriging Interpolation in Modelling Tropospheric Wet Delay |
title_short |
Kriging Interpolation in Modelling Tropospheric Wet Delay |
title_full |
Kriging Interpolation in Modelling Tropospheric Wet Delay |
title_fullStr |
Kriging Interpolation in Modelling Tropospheric Wet Delay |
title_full_unstemmed |
Kriging Interpolation in Modelling Tropospheric Wet Delay |
title_sort |
kriging interpolation in modelling tropospheric wet delay |
publisher |
MDPI AG |
series |
Atmosphere |
issn |
2073-4433 |
publishDate |
2020-10-01 |
description |
This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to evaluate the performances of the Kriging interpolation, a sparse network with 8 stations and a dense network with 19 stations from continuously operating reference stations (CORS) of the Netherlands are selected as the reference. In addition, other 15 CORS stations are selected as users, which are divided into three blocks: 5 stations located approximately in the center of the networks, 5 stations on the edge of the networks and 5 stations outside the networks. The zenith tropospheric wet delays are estimated at the network and user stations through the ionosphere-free positioning model; meanwhile, the predicted wet delays at the user stations are generated by the Kriging interpolation in the use of the tropospheric estimations at the network. The root mean square errors (RMSE) are calculated by comparing the predicted wet delays and estimated wet delays at the same user station. The results show that RMSEs of the stations inside the network are at a sub-centimeter level with an average value of 0.74 cm in the sparse network and 0.69 cm in the dense network. The stations on edge and outside the network can also achieve 1-cm level accuracy, which overcomes the limitation that accurate interpolations can only be attained inside the network. This contribution also presents an insignificant improvement of the prediction accuracy from the sparse network to the dense network over 1-year’s data processing and a seasonal effect on the tropospheric wet delay predictions. |
topic |
GNSS troposphere tropospheric delay kriging interpolation precise point positioning |
url |
https://www.mdpi.com/2073-4433/11/10/1125 |
work_keys_str_mv |
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