Modeling precipitable water vapour using Global Navigation Satellite System data over the East African region

Abstract

The Global Navigation Satellite System (GNSS) can be used to derive accurately the Zenith Tropospheric Delay (ZTD) and Precipitable Water Vapour (PWV) under all-weather conditions. The derived ZTD and PWV values play a vital role in climate studies, weather forecasting and are operationally assimilated into numerical weather prediction models. In this study, GNSS data for derivation of ZTD and PWV was collected from 13 geodetic permanent stations for the years 2013 to 2016 over the East African tropical region. The 13 stations consist of 5 International GNSS Service (IGS) stations, 4 Africa Array (AA) stations, and 4 Malawi Rifting stations from Uganda, Kenya, Tanzania and Rwanda. The ZTD time series were processed using goGPS software version 1.0 beta1, a MATLAB based GNSS processing software, developed for kinematic and quasi static applications. The derived goGPS ZTDs were compared to the values derived from the GIPSY-OASIS via Jet Propulsion Laboratory (JPL) online Automatic Precise Positioning Service (APPS) to access their accuracy. The derived goGPS ZTDs were also validated using the Unified Environmental Modeling System (UEMS) numerical weather prediction (NWP) model. The agreement between the goGPS ZTD and the UEMS NWP ZTD indicates that goGPS ZTD can be assimilated into NWP models in the East African region. For this study, only five out of 13 GPS stations used are equipped with meteorological sensors. Alternatively, the interpolated European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) dataset were also used. The ERA5 dataset at these locations was first validated using the meteorological data from the sensors available at the five stations. The interpolation and extrapolation of ERA5 data generated surface meteorological parameters with acceptable accuracies demonstrating that it can serve as a complement to the present ground based GPS meteorological observation networks. The determination of GPS PWV also requires information on the weighted mean temperature (Tm); the Tm at the 13 GPS stations was evaluated using three methods: the temperature and humidity profiles from ERA5 (ERA5 Tm), as well as the Tm derived from Bevis and Yao Tm - Ts relationships. The derived GPS PWV datasets were validated using the ERA5 PWV. GPS PWV values were used as the reference and the Root Mean Square errors (RMSE) of the two PWV values are in the range 1.35 mm to 2.25 mm with the overall average value of 1.66 mm. In this study, PWV, pressure, temperature and weighted mean temperature models have been developed from GPS and ERA5 data. The purpose of the developed models is to predict PW over regions with data gaps where the computation of Zenith Tropospheric Delays (ZTD) is impossible and in cases of station outages. In addition, the models will provide meteorological parameter where meteorological sensors are missing. Based on the RMSE, it was observed that the site-specific models developed can be used to provide estimates of almost the same level of accuracy compared to the measured values at the 13 stations. Despite having scattered GNSS stations in the region with data gaps, the PWV retrieval from ZTDs has been achieved.