| Summary: | Precise measurement of atmospheric water vapour has been very challenging due to some limitations of the conventional meteorological systems. Hence, there is a need for Global Positioning System (GPS) for meteorology or GPS meteorology. Therefore, the ground-based GPS meteorology and the space-based GPS Radio Occultation (GPS RO) techniques have been used. The major challenges of groundbased GPS meteorology approach include the lack of surface meteorological data collocating with the location of the ground-based GPS receivers as well as its inability to profile the atmosphere. Whereas the GPS RO technique has a problem of generating profile for the lower tropospheric region which holds the largest amount of water vapour. This research investigates an approach for estimating wet refractivity profile using GPS data. Three specific objectives were set for the study which was conducted in three phases. The first objective assessed GPS Integrated Water Vapour (GPS IWV) in which GPS IWV from interpolated meteorological data and the applicability of Global Pressure and Temperature (GPT2w) model for GPS meteorology was evaluated. The results revealed that the GPS IWV from Automatic Weather Station (AWS) presents good correlation with the radiosonde IWV, the standard deviation of the biases vary spatially from 3.162kg/m2 to 3.878 kg/m2. The actual influence of the errors of GPT2w meteorological parameters on GPT2w-based GPS IWV lies between 2kg/m2 and 3kg/m2, translating to an average relative accuracy of 1.2%. Meanwhile, the sensitivity of the GPS RO data to equatorial water vapour trend was evaluated to achieve second objective. It was found that the GPS RO IWV is highly comparable with the ground-based GPS IWV, having average bias of 1.8kg/m2. Finally, a methodology for GPS wet refractivity retrieval was developed towards achieving the third objective of this research. The Modified Single Exponential Function (MSEF) model for retrieving wet refractivity profile from ground-based GPS Zenith Wet Delay (ZWD) was realised. The output validation using profile from radiosonde and GPS RO observations showed high correlation in each case. In order to improve the performance of the MSEF model, an approach for integrating the ground-based and the space-based GPS data (GIWRef) was formulated. The GIWRef profile is highly correlated with the GPS RO profile, which showed an average improvement of 41% over the initial MSEF method with average correlation coefficient of 0.99. It can be concluded from the foregoing results of the study that the MSEF and GIWREF concepts developed in this work, presents a potential for augmenting weather forecasting and monitoring water vapour system.
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