Abstract
Stratiform and convective rain types are associated with different 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 different precipitation types. Second, the same unclassified samples are classified with different 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 great potential in classifying precipitation types.
Highlights
Precipitation can be divided into stratiform precipitation and convective precipitation [1]
The K-nearest neighbor (KNN) classification results were compared with the 2A23 product, and the results were evaluated based on the probability of detection (POD), false alarm rate (FAR), and cumulative success index (CSI): POD
A KNN supervised machine learning algorithm is used in this paper to classify precipitation types with ground-based radar data
Summary
Precipitation can be divided into stratiform precipitation and convective precipitation [1]. An extended SHY95 method was applied by DeMott, et al [16], who used a two-dimensional BET at each height level within a volume of radar reflectivity to extend this approach to three dimensions They suggested that using low-level data may lead to the misclassification of convective cells that tilted strongly with height and showed that using three-dimensional data can improve the accuracy of precipitation classification. Instead of using the traditional method based on the BET, Anagnostou [18] proposed an algorithm for classifying stratiform and convective clouds using an artificial neural network (ANN). KNN has fewer tunable parameters and provides faster calculations for small data sets than other methods This approach has advantages in solving classification problems involving precipitation types.
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