Abstract: Based on the original backscattering signals of the micro rain radar and a new micro rain radar processing methodology (RaProM), the equivalent radar reflectivity, the particle falling velocity, and Doppler spectrum width are calculated after power spectrum calculation, noise removal, and dealiasing. Furthermore, the precipitation types are identified. The RaProM algorithm can identify particle phases, such as snow, drizzle, rain, hail, and their mixed types, considering particle falling velocity, equivalent radar reflectivity, particle size characteristics of different precipitation types, and presence of bright bands. In addition, liquid precipitation parameters, such as radar reflectivity factor and rain intensity, are computed. Subsequently, three typical cases of stratiform cloud precipitation on July 2, 2021, transition of rain and snow on December 25, 2019, and the precipitation with the height of the zero degree layer decreasing gradually on March 4, 2018, are selected to verify and discuss the results. The method of precipitation type classification is applied to typical stratiform precipitation, the vertical structure shows snowflakes in the supercooled water area, mixed-type precipitation in the ice-liquid conversion zone near the 0°C layer, and liquid precipitation below the bright band, proving the validity of the method. The methods are then applied to precipitation type classification and bright band detection. The results show that the RaProM algorithm has the advantage of making no assumptions about precipitation type and considering particle upward velocity (such as snowflakes) over the standard inversion process of micro rain radar. The RaProM algorithm results are in good agreement with the colocated microwave radiometer and cloud radar in the vertical structure, and the deviations from the ground disdrometer in raindrop size distribution and rain intensity are reduced compared with the products of micro rain radar.