Characterize Temperature and Water Vapor Structure in PBL with Joint GNSS Radio Occultation (GNSS-RO) and Passive Microwave Radiometer (MWR) Retrieval

Characterizing the thermodynamic structure of Planetary Boundary Layer (PBL) from the spaceborne platforms is challenging because of its highly variable moisture distribution that demands high resolution observations in both vertical and horizontal dimensions. Here we combine two complementary spaceborne sounding observations: Global Navigation Satellite System – Radio Occultation (GNSS-RO) and passive Microwave Radiometer (MWR) to estimate the PBL temperature and water vapor structures. GNSS-RO provides high vertical resolution (~200m) but poor horizontal resolution (>100km) measurements, and the temperature and moisture retrievals are coupled so that the a-priori information is required to retrieve them independently. On the other hand, the MWR measures 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, MWR is limited by poor vertical resolution (>2km), precipitation, and surface characteristic uncertainty over land.

In this presentation two different approaches to combine GNSS-RO and MWR data will be discussed. First, an optimal estimation method, 1DVar, is implemented to combine the collocated GNSS-RO refractivity or bending angle and MWR TB observations in 1D. By applying 1DVar to the simulated RO/MWR observations this method is shown to reduce GNSS-RO temperature and water vapor retrieval biases at the top of PBL, and simultaneously capture the fine-scale variability that MWR cannot resolve. Second, a tomographical 3D combination approach over the vertical plane of GNSS-RO raypath will be presented. Through 3D ray tracer the water vapor quantity of each voxel along the ray path can be related to the GNSS-RO excess phase measurements, and the horizontal water vapor distribution is preserved as a-priori using MWR retrievals. This method has been tested using mesoscale model output from Weather Research and Forecasting Model (WRF) simulations and actual RO/MWR onservations. The results show that the joint retrieval can better resolve the complex moisture structure than what is possible from either measurement alone.

Characterizing the thermodynamic structure of Planetary Boundary Layer (PBL) from the spaceborne platforms is challenging because of its highly variable moisture distribution that demands high resolution observations in both vertical and horizontal dimensions. Here we combine two complementary spaceborne sounding observations: Global Navigation Satellite System – Radio Occultation (GNSS-RO) and passive Microwave Radiometer (MWR) to estimate the PBL temperature and water vapor structures. GNSS-RO provides high vertical resolution (~200m) but poor horizontal resolution (>100km) measurements, and the temperature and moisture retrievals are coupled so that the a-priori information is required to retrieve them independently. On the other hand, the MWR measures 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, MWR is limited by poor vertical resolution (>2km), precipitation, and surface characteristic uncertainty over land.

In this presentation two different approaches to combine GNSS-RO and MWR data will be discussed. First, an optimal estimation method, 1DVar, is implemented to combine the collocated GNSS-RO refractivity or bending angle and MWR TB observations in 1D. By applying 1DVar to the simulated RO/MWR observations this method is shown to reduce GNSS-RO temperature and water vapor retrieval biases at the top of PBL, and simultaneously capture the fine-scale variability that MWR cannot resolve. Second, a tomographical 3D combination approach over the vertical plane of GNSS-RO raypath will be presented. Through 3D ray tracer the water vapor quantity of each voxel along the ray path can be related to the GNSS-RO excess phase measurements, and the horizontal water vapor distribution is preserved as a-priori using MWR retrievals. This method has been tested using mesoscale model output from Weather Research and Forecasting Model (WRF) simulations and actual RO/MWR onservations. The results show that the joint retrieval can better resolve the complex moisture structure than what is possible from either measurement alone.