The accuracy of surface meteorological measurements is vital to derive accurate Global Positioning System (GPS) Precipitable Water Vapour (PWV) data. However, in absence of surface meteorological data, data from Numerical Weather Prediction Models (NWP) are used. The accuracy of these models varies depending on the model, region, season and other climatic conditions. In this study, GPS data for derivation of PWV is collected from 13 geodetic permanent stations for the years 2013 to 2016. Five out of 13 GPS stations are equipped with meteorological sensors. The interpolated European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) dataset at these locations is first validated using the meteorological data from these sensors. The assessment shows that the average root mean square errors (RMSE) of surface pressure and temperature values are about 0.72 hPa and 1.04 K respectively. PWV determined also requires information on the weighted mean temperature (Tm); the Tm at the 13 GPS stations was also 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 Tm derived from ERA5 was used as a reference to assess the errors of the Bevis and Yao Tm. The comparisons show that the average RMSE values of Tm derived from the two methods are 2.05 K and 2.02 K respectively. The GPS PWVs calculated using the ERA5 interpolated data (GPS ERA5 PWV) were compared to GPS PWV calculated using the sensor meteorological data (GPS MET PWV) at the 5 stations. The GPS ERA5 PWV values were used as the reference. The RMSE values of the two PWV datasets are in the range of 0.19 mm to 0.90 mm with overall average value of 0.44 mm. Also the computed ERA5 PWV data are compared to GPS ERA5 PWV at the 13 stations. ERA5 PWV values were used as the reference and the RMS errors 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. Regarding the seasonal variations, the stations between latitudes 4 °S and 4 °N and latitudes 12 °S to 4 °S have the highest PWV values in spring and summer while the lowest values occur in summer and winter respectively.@en

The Global Navigation Satellite System (GNSS) can be used to derive accurately the Zenith Tropospheric Delay (ZTD) under all-weather conditions. The derived ZTDs play a vital role in climate studies, weather forecasting and are operationally assimilated into numerical weather prediction models. In this study, variations and statistical analysis of GNSS-derived ZTD over the East African tropical region are analysed. The data is collected from 13 geodetic permanent stations for the period of 4 years from 2013 to 2016. The 13 stations consist of 5 International GNSS Service (IGS) stations plus 8 stations as follows: 4 Africa Array 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, originally developed for kinematic applications but recently re-engineered for quasi static applications. The annual variation of the ZTD time series was investigated using Lomb Scargle periodograms. The semi-annual frequency has the dominant power in subregion 1 (latitudes 4 °S and 4 °N) and the annual frequency has the dominant power in subregion 2 (latitudes 12 °S to 4 °S). The highest ZTD estimates occur during the rainy seasons, at all stations, and the lowest estimates occur during the dry seasons. The results also show that the ZTD estimates are largest at stations located at low elevation (regions close to the Indian Ocean). The derived ZTDs are compared to the values derived from the GIPSY-OASIS via Jet Propulsion Laboratory (JPL) online Automatic Precise Positioning Service (APPS) and the Unified Environmental Modelling System (UEMS) numerical weather prediction (NWP) model. The comparison of goGPS and APPS ZTD at the 13 stations shows an overall average bias, Root Mean Square (RMS) and standard deviation (stdev) of −0.9 mm, 3.2 mm and 3.0 mm respectively, with correlation coefficients ranging from 0.974 to 0.999. The comparison of goGPS ZTD against UEMS NWP ZTD at 8 selected stations shows average bias, RMS and stdev of −12.4 mm, 22.0 mm and 17.6 mm respectively, with correlation coefficients ranging from 0.802 to 0.974. The agreement between the GPS ZTD and the NWP ZTD indicates that goGPS ZTD can be assimilated into NWP models in the East African region.@en