Global Navigation Satellite (GNSS) Tropospheric Delay Estimation in Offa, Kwara State, Nigeria

This Hydrostatic delay is caused by the dry component in the atmosphere, while wet delay is caused exclusively by highly varying water vapor in the atmosphere. The hydrostatic delay makes up approximately 90% of the total tropospheric delay and depends entirely on the atmospheric weather characteristics in the troposphere. The hydrostatic delay in the zenith direction is characteristically about 2.30m. It is modelled using measurements of surface temperature and pressure. However, the wet delay depends on water vapor pressure, which is highly variable in space and time and cannot be modelled precisely with surface measurements. Most global tropospheric delay models used vary from one latitudinal location to another and usually return it in the zenith direction which is then mapped to the slant. This study estimates the tropospheric delay for Offa using monthly mean tropospheric data for the years 2014 to 2024 obtained from the Nigerian Meteorological Agency (NIMET). Three tropospheric models, namely; the Saastamoinen model (driven by tropospheric data) called SAAS MODE 1, the Saastamoinen model (driven by default values) called SAAS MODE2 and UNB3M models were used, and results were compared. Here the obtained zenith delay is mapped from vertical to slant using the Chao, Niell, and TANE mapping function at an elevation angle of 2° to 90°. The results obtained show that there is a seasonal trend in SAAS MODE1 whereas it is not observed in SAAS MODE 2 and UNB3M. Two peaks are observed generally in the value of zenith tropospheric delay for SAAS MODE 1 for all the years, one in March and the other one in November with a kind of minimum in August. In the exceptional, especially years 2019 and 2020 where three peaks are observed in the values of zenith tropospheric delay for SAASMODE1, one in March and the others in February and October with a kind of minimum in August. Generally, there is a drop from the peak point in March to July there about and later rise around August the minimum point. To compare the output of three mapping functions (Chao, Niell, and TANE) at elevations of 2° to 90°, SAAS MODE 1 and SAAS MODE 2 were used for year 2024 data. It is observed that the three mapping functions returned almost the same values from elevation 90° to about 10°. Major discrepancies are observed at an elevation of 5° and below in which the TANE value deviated completely from the other two models. This gives Chao and Niell a good edge over TANE.As far as the deviations are concerned, the TANE value is higher than the other two models by about30 m at the elevation angle of 2°. From the results obtained, the Saastamoinen model (driven by tropospheric data) is more realistic being varies with season most especially the wet component