Prediction of troposphere wet delay

Abstract

The wet delay is one of the most difficult sources of error to quantify in global navigation satellite systems (GNSS) due to its spatial and temporal variability. However, its estimation is vital in some applications, such as in Precise Point Positioning (PPP), network real-time kinematic and weather forecast. Such estimation is usually carried out for its projected component along the zenith, defined as the zenith wet delay (ZWD). Prediction of the ZWD is important if there is a break in its estimation, and in providing initial values for future processing. In this paper, prediction modeling for the ZWD is investigated. An autocorrelation study was first performed on ZWD data over three different days at the international GNSS service station ONSALA to provide an insight into the temporal correlations among ZWDs. The choice of this station was based on the availability of reliable ZWD data measured by water vapor radiometers, which provide a reference for assessment of the accuracy of predicted values. Results have shown that successive ZWD estimates are significantly correlated for up to 1.7 hours. Different trend and smoothing prediction models were then investigated. Since prediction of ZWD requires continuous data sequence, interpolation methods of possible missing ZWD values are discussed. The use of linear interpolation or a cubic Hermite polynomial was found to be sufficient for interpolation of ZWD. Test results of prediction models show that the single-exponential smoothing model was the best performer where ZWD estimates were forecasted with a root mean square error of less than 1 cm for up to two hours.