## 1 Introduction  
Water vapor is the Earth's most important greenhouse gas as it accounts for nearly two-thirds of the natural greenhouse effect [1]. It plays a major role in the global energy cycle as a dominant feedback variable in association with radiative effects and moist dynamics [2], and fast-acting water vapor feedbacks constitute a strong amplification mechanism for anthropogenic climate change, which makes water vapor a key parameter for climate change analysis [3]. Water vapor also carries a large amount of latent heat which is released into the atmosphere during condensation and stored again through evaporation [4]. This results in the vertical distribution of tropospheric water vapor controlling many aspects of the climate, especially through its influence on shallow and deep convection throughout the tropics [5]. In contrast to other greenhouse gases such as carbon dioxide or methane, water vapor has a much higher temporal and spatial variability [6]. Thus, accurate and consistent tropospheric water vapor measurements are essential for studying water vapor feedbacks on the global energy budget, which is still one of the largest uncertainties in understanding global warming [7]. To address this need, atmospheric scientists have developed many tools to measure the vertical and horizontal distribution of water vapor, although each method has limitations. Historically, water vapor has been measured using radiosondes, which allow for the direct measurement of moisture with a high vertical resolution at each launch station. However, there are only about 1000 radiosonde launch stations around the world which are sparsely distributed (e.g., mostly over land), typically launched only twice per day, and systematic bias exists depending on the type of instrumentation used [8; 9]. Various water vapor remote sensing instruments have also been developed over the past 40 years, such as infrared and microwave radiometers, which allow for more complete spatial and temporal coverage than radiosondes. The drawbacks of these methods are that infrared radiometers are limited to clear-sky conditions [10] and the accuracy of microwave radiometer water vapor retrievals can be affected over different surface types due to assumptions made about surface emissivity [11]. More recently, global navigation satellite system (GNSS) radio occultation (RO) has been demonstrated to accurately measure water vapor with a high vertical resolution in all weather conditions, regardless of surface type [12; 13; 14; 15; 16]. However, issues can arise within the moist lower troposphere. Negative RO refractivity bias commonly occurs within the boundary layer due to a combination of various receiver tracking errors and the presence of super-refraction [17; 18; 19; 20]. For example, negative biases in bending angles and refractivity may be caused by errors in profiles with a low signal-to-noise ratio (SNR) within the complex moist lower troposphere [20]. Additionally, super-refraction of the RO signal occurs when the vertical refractivity gradient exceeds critical refraction (\(\mathrm{dN}/\mathrm{d}z<-157\) N-units km\({}^{-1}\)), which often results from the combination of a sharp vertical gradient in moisture and a temperature inversion frequently observed at the top of the boundary layer [21]. When lower tropospheric refractivity is negatively biased, this results in the underrepresentation of lower tropospheric moisture in the derived humidity profiles [17; 22; 23]. Thus, even when combining each of these observational methods, water vapor is generally observed insufficiently compared to other meteorological parameters [6]. Atmospheric reanalysis is a useful tool that addresses some of the observational limitations. Reanalyses aim to construct a continuous and complete picture of the weather and climate by constraining evolving model forecasts after assimilating a large mixture of observations with different spatial/temporal resolutions, accuracies, and degrees of correspondence to model state variables and fluxes [24].