Dealing with assumptions in microphysics and vertical distribution of hydrometeors on rainfall estimation from passive microwave sensors

Rainfall measurements from passive microwave sensors are based on a relationship between the observed brightness temperature and the integrated water and ice content in a cloud. The surface rainfall, however, depends upon details of the vertical structure of various hydrometeors and their microphysical properties. Physically-based retrieval algorithms often therefore make use of cloud resolving models (CRMs) to build a-priori databases of potential rain structures with assumed cloud microphysics. This study first reviews how the approximated microphysics cause uncertainties in surface rainfall estimations. Different microphysics schemes employed with the Weather Research and Forecasting (WRF) model are selected to quantify and compare retrieved rainfall amounts. We also discuss the effects of different vertical distributions associated with the similar surface rainfall considering slanted viewing configuration of current satellite microwave sensors.