Characterizing the moist thermodynamic structure of the lower troposphere (LT) from spaceborne observations is challenging. This is largely due to the LT's highly variable moisture distribution which requires high-resolution observations in both the vertical and horizontal dimensions. Two of the most impactful spaceborne remote sensing technologies in numerical weather prediction (NWP) are (1) the global navigation satellite system – radio occultation (GNSS-RO) and (2) nadir sounding instruments which include microwave radiometers (MWR) and infrared sounders (IR). These two technologies provide complementary information of atmospheric temperature and water vapor. GNSS-RO provides high vertical resolution (∼200 m) with near zero interference from hydrometeor, but its horizontal resolution along the ray path is coarse (>100 km). On the other hand, nadir sounders measure brightness temperature (TB) that can be related to the temperature and water vapor structure in the atmospheric column with higher horizontal resolution (∼25 km). However, retrievals from passive sounders have lower vertical resolution (>2 km), and are complicated by precipitation, clouds, and surface emissivity uncertainty over land.</p> <p>In this study we combine these two complementary types of observations by use of a novel tomography method to improve the 3D characterization of water vapor in the LT. This is done by adjusting the water vapor density along the RO links to match the RO observations, while preserving the horizontal water vapor variability based on information derived from the nadir sounders. We test this method using simulations based on mesoscale model outputs from the Weather Research and Forecasting Model (WRF), as well as real observations from RO and Cross-track Infrared Microwave Sounder Suite (CrIMSS). We demonstrate that the tomographic retrieval can resolve the complex moisture structure better than what is possible from either measurement alone.