Global Navigation Satellite System – Radio Occultation (GNSS-RO) and Microwave Radiometry (MWR) are two of the most impactful spaceborne remote sensing techniques for numerical weather prediction (NWP). These two techniques provide complementary information about atmospheric temperature and water vapor structure. GNSS-RO provides high vertical resolution measurements with cloud penetration capability, but the temperature and moisture are coupled in the GNSS-RO retrieval process and their separation requires the use of a-priori information or auxiliary observations. On the other hand, the MWR measures brightness temperature (Tb) in numerous frequency bands related to the temperature and water vapor structure, but is limited by poor vertical resolution (>2 km) and precipitation. In this study we combine these two technologies in an optimal estimation approach, 1D Variation method (1DVar), to better characterize the complex thermodynamic structures in the lower troposphere. This study employs both simulated and operational observations. GNSS-RO bending angle and MWR Tb observations are used as inputs to the joint retrieval, where bending can be modeled by an Abel integral and Tb can be modeled by a Radiative Transfer Model (RTM) that takes into account atmospheric absorption, and surface reflection and emission. By incorporating the forward operators into the 1DVar method, the strength of both techniques can be combined to bridge individual weaknesses. Applying 1DVar to the data simulated from Large Eddy Simulation (LES) is shown to reduce GNSS-RO temperature and water vapor retrieval biases at lower troposphere, while simultaneously capturing the fine-scale variability that MWR cannot resolve. A sensitivity analysis is also conducted to quantify the impact of the a-priori information and error covariance used in different retrieval scenarios. The applicability of 1DVar joint retrieval to the actual GNSS-RO and MWR observations is also demonstrated through combining collocated COSMIC-2 and Suomi-NPP measurements.
