Water vapor is a major driver of climate and weather and plays a key role in many atmospheric processes. While water is present in all three aggregation states in the atmosphere, the gaseous one dominates and water vapor is the most important natural greenhouse gas. Due to its large latent heat of vaporization/condensation, water plays a major role in the energy transport of the atmosphere. To accurately model weather and climate it is crucial to understand the distribution, transport, and vertical structure of humidity. However, measuring water vapor accurately is a challenge, as it is highly variable on both spatial and temporal scales. To this day, no single observing system can provide global accurate tropospherichumidity data with a resolution that captures its variability on all important vertical, horizontal, and time scales.The Global Positioning System (GPS) Radio Occultation (RO) method provides high vertical resolution humidity profiles for the troposphere. This measurement technique uses phase changes of GPS signals to derive atmospheric thermodynamic parameters. In the troposphere, ancillary data are required to retrieve humidity (or temperature).The objective of this thesis is to assess the quality of RO-derived humidity using other remote sensing techniques, in-situ observing techniques, and model analyses and reanalyses. The structural uncertainty of RO-derived humidity is determined from comparisons of multiple different RO humidity retrievals. Even in challenging humidity conditions, such as high variability and extreme dryness, the accuracy of RO-derived humidity data is similar to the one of other state-of-the-art humidity measurements. Additionally, the RO technique features global coverage,all-weather capability, and same data quality for day and night time measurements. This shows the usefulness of RO for tropospheric humidity studies, as well as its potential to contribute tropospheric data to NWP models via data assimilation.