Kriging interpolation in modelling tropospheric wet delay

Kriging interpolation in modelling tropospheric wet delay

Ma, H. (TU Delft Mathematical Geodesy and Positioning; Wuhan University)
Zhao, Qile (Wuhan University)
Verhagen, S. (TU Delft Mathematical Geodesy and Positioning)
Psychas, D.V. (TU Delft Mathematical Geodesy and Positioning; Fugro Innovation & Technology B.V.)
Dun, H. (TU Delft Mathematical Geodesy and Positioning)

2020

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.

GNSS
Kriging interpolation
Precise point positioning
Troposphere
Tropospheric delay

http://resolver.tudelft.nl/uuid:2fcea390-deb2-42ad-9039-291526b86cd1

https://doi.org/10.3390/atmos11101125

2073-4433

Atmosphere, 11 (10), 1-14

Institutional Repository

journal article

© 2020 H. Ma, Qile Zhao, S. Verhagen, D.V. Psychas, H. Dun