Expected Sounding Performances from a 1DVAR Retrieval Algorithm Applied to Simulated ATMS Observations over Precipitating Conditions

Challenges to conventional microwave sounding algorithms arise when radiometric observations are influenced by precipitation-sized hydrometeors or large ice crystals. While it is still possible to extract temperature and water vapor information from above the precipitating or icy layers, lower atmospheric information is masked by the scattering and emission caused by the presence of larger hydrometeors. The Microwave Integrated Retrieval System (MiRS) is a physical algorithm based on a one-dimensional variational (1DVAR) approach, which retrieves temperature, moisture and hydrometeor profiles, along with the surface emissivity spectrum simultaneously, and is applicable over all surface types and in all weather conditions. Currently, MiRS is run operationally at the National Oceanic and Atmospheric Administration for the NOAA-18, NOAA-19, and Metop-A AMSU/MHS sensors as well as the U. S. Department of Defense DMSP-F16 and F18 SSMI/S. In this study, we present expected sounding performances under precipitating conditions from MiRS when applied to simulated proxy data from the Advanced Technology Microwave Sounder (ATMS) which will be flown onboard the NPOESS Preparatory Project (NPP) and future Joint Polar Satellite System (JPSS) platforms. Specifically, the impact of utilizing dynamic constraints and radiometric observation bias corrections in the 1DVAR algorithm for various precipitation regimes on the sounding will be demonstrated.