*Article* **Convective**/**Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm**

#### **Zhida Yang, Peng Liu and Yi Yang \***

Research and Development Center of Earth System Model (RDCM), College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; yangzhd17@lzu.edu.cn (Z.Y.); liup16@lzu.edu.cn (P.L.) **\***Correspondence:yangyi@lzu.edu.cn

Received: 2 August 2019; Accepted: 25 September 2019; Published: 29 September 2019

**Abstract:** Stratiform and convective rain types are associated with di fferent cloud physical processes, vertical structures, thermodynamic influences and precipitation types. Distinguishing convective and stratiform systems is beneficial to meteorology research and weather forecasting. However, there is no clear boundary between stratiform and convective precipitation. In this study, a machine learning algorithm, K-nearest neighbor (KNN), is used to classify precipitation types. Six Doppler radar (WSR-98D/SA) data sets from Jiangsu, Guangzhou and Anhui Provinces in China were used as training and classification samples, and the 2A23 product of the Tropical Precipitation Measurement Mission (TRMM) was used to obtain the training labels and evaluate the classification performance. Classifying precipitation types using KNN requires three steps. First, features are selected from the radar data by comparing the range of each variable for di fferent precipitation types. Second, the same unclassified samples are classified with di fferent k values to choose the best-performing k. Finally, the unclassified samples are put into the KNN algorithm with the best k to classify precipitation types, and the classification performance is evaluated. Three types of cases, squall line, embedded convective and stratiform cases, are classified by KNN. The KNN method can accurately classify the location and area of stratiform and convective systems. For stratiform classifications, KNN has a 95% probability of detection, 8% false alarm rate, and 87% cumulative success index; for convective classifications, KNN yields a 78% probability of detection, a 13% false alarm rate, and a 69% cumulative success index. These results imply that KNN can correctly classify almost all stratiform precipitation and most convective precipitation types. This result suggests that KNN has grea<sup>t</sup> potential in classifying precipitation types.

**Keywords:** precipitation classification; K-nearest neighbor; Doppler radar; Tropical Precipitation Measurement Mission (TRMM)
