Faculty of Health, Engineering and Science
School of Engineering
Proper dynamic modelling of the troposphere wet delay using the Global Navigation Satellite Systems (GNSS) measurements is important in precise point positioning and in estimation of the PrecipitableWater Vapour (PWV) for weather forecast. The random walk (RW) and the first-order Gauss-Markov (GM) autocorrelation models are commonly used for this purpose. However, it was found that these models consistently underestimate the temporal correlations that exist among the troposphere wet delay. Therefore, a new dynamic model is proposed. The performance of the proposed model in following the autocorrelation of actual data is demonstrated and its impact on the near-real time estimation of the wet delay was tested and compared to that of the GMand RWmodels. Results showed that the proposed model outperformed these models. When the computed wet delays were used to compute PWV, their estimated valueswere very close to actual PWV data measured by radiosonde with differences less than 1 mm.