Artificial neural network for predicting global sub-daily tropospheric wet delay

Abstract

Zenith Wet Delay (ZWD) represents an important parameter in the Global Navigation Satellite Systems (GNSS) positioning, meteorology, and weather forecasting as it assembled to the Integrated Water Vapour (IWV) that is important for weather prediction. It is difficult to be predicted due to its temporal and spatial resolution. With the increasing development in machine learning, Artificial Neural Network (ANN) started to be used for predicting different nonlinear problems. In this paper, ANN is used for predicting ZWD from previous epoch of temperature, pressure, and Water Vapour Pressure (WVP) using twelve years (2008–2019) of data from 505 globally distributed stations. Four scenarios were followed in this study, using previous epochs from 6, 12, 18 and 24 h lags, as input for ANN. The results showed that ZWD can be predicted using ANN for all the stations using the 6-h lag scenario at the published required level of accuracy, 3.0 cm Root Mean Square Error (RMSE) and 60.2% of them have RMSE lower than 1.5 cm with 97.6% of them have the correlation coefficient (R) values ≥ 0.9. Although the accuracy of the remaining three scenarios was found to be, as expected, lower than the first scenario, their predicted ZWD showed promising R-values ≥ 0.9 to be 78.6%, 64.1% and 58.2% of the stations, for the three remaining scenarios respectively. While 68.5%, 68.9% and 65.7% of the stations were below 3.0 cm RMSE. The conclusion is that, the ANN with the suggested strategy can produce a reliable ZWD. The finding of this paper could help to improve the predicted ZWD for GNSS positioning and meteorology as well as weather forecasting.