Forecasting of tropospheric parameters using meteorological data and machine learning

Radio signals sent by Global Navigation Satellite System (GNSS) satellites are received by GNSS stations on Earth. The signals get delayed as they propagate through the troposphere, this delay can be measured and thus, tropospheric properties can be estimated.

The total tropospheric delay in zenith direction is split into a zenith hydrostatic delay (ZHD) and a zenith wet delay (ZWD). While the ZHD can be modelled analytically with high accuracy, the ZWD is more difficult to model and is therefore typically estimated empirically. Estimating ZWD with high accuracy is important because it is one of the major error sources for GNSS positioning. Furthermore, the ZWD is highly correlated to the water vapour content along the signal path and thus, interesting for GNSS meteorology. Therefore, many studies have investigated new methods to improve state-of-the-art ZWD models. Recently, also machine learning (ML) approaches have been used to create tropospheric delay models. In addition to modelling ZWD, forecasting of ZWD is of great importance. Due to the relation of ZWD to water vapour, accurate ZWD forecasts would be essential for weather forecasting.

The aim of this work is to develop a global ML-based model capable of forecasting ZWD for the next 24 hours at any point on Earth. It is trained on ZWDs, provided by the Nevada Geodetic Laboratory,  from over 10'000 GNSS stations and evaluated on ZWDs of 2700 test stations. The model utilizes the geographical location of the GNSS station and meteorological data from the ERA5 data set as its input features. To make hourly ZWD forecasts for the next 24 hours, forecasts of the meteorological data are taken from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). Preliminary results using Extreme Gradient Boosting (XGBoost) show an average root mean squared error of around 1.5 cm over all testing stations for a forecasting horizon of 24 hours per day.