Determination of the best-fit Tropospheric Delay Model on the Nigerian Permanent GNSS Network

Abstract

The Federal Government of Nigeria through the Office of the Surveyor General of the Federation (OSGoF) set up surveying infrastructure throughout the country known as the NIGerian Reference GNSS NETwork (NIGNET). The NIGNET is a network of Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) set up at different locations in Nigeria for surveying and mapping. They are satellite tracking stations operating 24 hours a day providing positional solutions. As signals from the satellite pass through the different layers of the atmosphere (ionosphere and troposphere), they are refracted thus, causing delay on the arrival of the signal, which in-turns affect positioning in the horizontal and height component. The most dominant spatially correlated bias is the tropospheric effect on the GNSS satellite signals. Several global tropospheric delay models are in use by different countries to mitigate the biases cause by the troposphere. This study therefore aim to determine the best-fit tropospheric delay model for the NIGerian GNSS Reference NET work (NIGNET) using data collected from the NIGNET stations across Nigeria. Three different global tropospheric models, namely; the Saastamoinen model, Hopfield model and Niell models were used, and results compared. Four processing strategies were adopted. The first strategy was without the application of any of the models, while in the second, third and fourth strategies, the GNSS data were processed with the application of each of the models. The results indicate that, the Niell model has the lowest mean zenith tropospheric delay (ZTD) of 2.330m with root mean square error (rmse) of 0.45m, while the Hopfield and Saastamoinen models have ZTD of 2.386m and 2.398m with rmse value of 0.60m and 0.71m respectively. On the overall, the Niell model has better performance in the network. This suggests that, the application of Niell model in the processing of all GNSS data will give a more reliable result in the position domain as well as the height component. The results are very useful to surveyors and geodesist engaged in surveying and mapping, and spatial positioning of infrastructures. It will enhance the effectiveness and reliability of the tropospheric delay resolution process for regional Global Positioning System (GPS) network users.