This study develops and tests a quality control (QC) algorithm for reflectivity from the single polarization phased array weather radar (PAWR) in Osaka, with particular focus on clutter detection, in preparation for radar data assimilation into a high resolution numerical model. The QC algorithm employs a Bayesian classification that combines the information from different parameters based on reflectivity and radial velocity. To take advantage of PAWR's unique high temporal and vertical resolutions, a new parameter based on the temporal variability of reflectivity is included. In addition, clutter probability estimations from previous volume scans are also included. The newly developed QC algorithm performs properly in two events characterized by heavy convective precipitation and stratiform precipitation.