No abstract

The Global Navigation Satellite System (GNSS) can be used to derive accurately the Zenith Tropospheric Delay (ZTD) under allweather 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 À0.9 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 À12.4 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.

Currently, the East African tropical region has limited information about Precipitable Water Vapour (PWV) data and yet the region has a high potential for its utilization. This is on the grounds that the East African tropical region is profoundly prone to climate change and fluctuation. Existing studies need data on the detailing and performance evaluation of precipitable water vapour models within East Africa. This has been so as a result of the scattered Global Positioning System (GPS) networks and other alternative water vapour measuring equipments, enormous information gaps and the absence of surface meteorological data. The accessibility and precision of surface meteorological estimations is crucial in deriving accurate GPS PWV data. In this study, the daily average, PWV, pressure, temperature and weighted mean temperature () models have been developed utilizing one year (2013) GPS PWV and European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re- Analysis PWV (ERA5 PWV), total column water vapour (TCWV), surface pressure and 2 meter (2m) temperature data. The purpose of the developed models is to predict PWV over regions with data gaps where the computation of GPS 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. The GPS PWV accuracy obtained with the developed models shows an average RMSE of 1.54 mm and MnB of 0.32 mm in comparison to the measured GPS PWV data. The ERA5 PWV accuracy obtained with the developed models shows an average RMSE of 0.33 mm and MnB of 0.01 mm in comparison to the measured ERA5 PWV data. Based on the RMSE, it was observed that the site-specific models developed can be utilized to provide estimates of nearly a similar degree of precision compared to the measured values at the thirteen stations.