Abstract: In order to give full play to the advantages of radar-gauge calibration algorithms, the correlation of radar data and rain gauge data is studied before and after quality control by analyzing the quality of radar data and rain gauge data. Based on 11 major precipitation processes during 2008—2009, impacts of 14 types of rain gauge densities on radar rainfall estimation are analyzed by using three radar-gauge calibration algorithms, which are variational calibration method, optimal interpolation method and Kalman filter method. The results show that the quality of rain gauge precipitation data of Tianjin is reliable, only 0.5% of the rain gauge precipitation data has larger error, and equipment failure or external factors is the main cause of its larger error. A reflectivity quality control (QC) procedure has been developed by Chinese Academy of Meteorological Sciences for identifying and removing non-precipitation echoes (such as ground clutter or anomalously propagated ground returns) from the radar base reflectivity fields, and quality control of radar data is implemented using the QC procedure. These non-meteorological echoes can be effectively removed, while retaining precipitation echoes, and thus the rainfall overestimation phenomenon of radar can be significantly improved. The correlation of radar reflectivity data and rain gauge data is analyzed before and after controlling their qualities by selecting different types of precipitation process, results show that quality control of the radar and rain gauge data is necessary to significantly increase the correlation between them and to improve the capacity of radar rainfall estimation. Using some radar-gauge calibration algorithms, impacts of different rain gauge densities on radar rainfall estimation are analyzed. The conclusion is that the capacity of radar rainfall estimation on rain gauge calibration can be improved significantly. The precision of radar rainfall estimation is continuously improved and then become stable with the density of rain gauge increased. The impacts of radar rainfall estimation and the calibration gauge density are related to the types of rainfall. To achieve equal calibration results, convective precipitation caused by cumulus needs the rain gauge density of about a gauge per 182 km², mixed cloud precipitation needs about a gauge per 211 km², and for stratiform precipitation a gauge per 405 km² is enough. The impacts of different radar-gauge calibration algorithms are different. It shows that Kalman filter method is suitable for the calibration of stratiform precipitation or for the low rain gauge density area, and variational method and optimal interpolation method are suitable for the calibration of convective precipitation or for the high rain gauge density area.