The path delay between a GPS satellite and a ground based GPS receiver depends, after elimination of ionospheric effects using a combination of the two GPS frequencies, on the integral effect of the densities of dry air and water vapour along the signal path. The total delay in the signal from each satellite is known as the slant delay as the path is most likely to be non-azimuthal. The slant paths are then transferred into the vertical (or zenith) by an elevation mapping function, and this new parameter is known as the Zenith Total Delay or ZTD. ZTD gives a measure for the integrated tropospheric condition and is now widely accepted as a standard product from a network of dual frequency GPS receivers. With further calculation, taking into account surface pressure and temperature, we can then convert a portion of the ZTD into an estimate of the Integrated Water Vapour content of the atmosphere (IWV). As IWV may potentially change rapidly on a very short timescale, it is the speed at which IWV can be calculated which is of critical importance to short term meteorological forecasting. Often, rapid changes in IWV are associated with high humidity conditions caused by extreme weather events such as thunderstorms. Extreme weather events such as these are typically difficult to predict and track under current operational meteorological systems and, as they have the potential to cause great damage, it is in the interests to both the public and Met Services to significantly improve nowcasting wherever possible. As such the requirement for dense near real-time GPS networks for meteorological applications becomes apparent. Furthermore water vapour is one of the most important constituents of the atmosphere as moisture and latent heat are primarily transmitted through the water vapour phase. As well as this, water vapour is one of the most important greenhouse gases, and as such accurate monitoring of water vapour is of great importance to climatological research. This thesis assesses the quality of GPS water vapour estimates by comparison against a number of other remote sensing instruments to determine what the true value of the water vapour is and how well GPS water vapour estimates accurately represent real atmospheric fluctuations. Through these comparisons we can derive site specific bias corrections and thus, reconstruct a bias corrected time series of data for climate applications. Furthermore to ensure all biases associated with GPS processing changes are removed, a long time series of raw GPS data has been reprocessed under a consistent processing routine to again identify any climate trends in the data. Finally, this thesis addresses the question of whether near real-time GPS derived tropospheric estimates are of sufficient quality for climate applications without the need for time consuming reprocessing.