Separation of Convective and Stratiform Precipitation Using Polarimetric Radar Data with A Support Vector Machine Method

Abstract. A precipitation separation approach using support vector machine method was developed and tested on a C-band polarimetric radar located in Taiwan (RCMK). Different from some existing separation methods that require a whole volume radar data, the proposed approach utilizes the polarimetric radar data from the lowest tilt to classify precipitation echoes into either stratiform or convective type. Through a support vector machine method, the inputs of radar reflectivity, differential reflectivity, and the separation index are utilized in the classification. The feature vector and weight vector in the support vector machine were optimized using well-classified training data. The proposed approach was tested with multiple precipitation events including two widespread mixture of stratiform and convective events, a tropical typhoon precipitation event, and a stratiform precipitation event. In the evaluation, the results from the multi-radar-multi-sensor (MRMS) precipitation classification approach were used as the ground truth, and the performances from proposed approach were further compared with the approach using separation index only with different thresholds. It was found that the proposed method can accurately identify the convective cells from stratiform storms with the radar data only from the lowest scanning tilt. It can produce better results than using the separation index only.