The Identification of Hail Storms in the Early Stage Using Time Series Analysis

Abstract

AbstractThis study investigates the characteristics of hail storms and cumulonimbus storms in China from 2005 to 2016. Ten features are proposed to identify storm cells that can produce hail, especially in the early stage of hail formation. These features describe hail storms based on three factors: the height and thickness of the cell core, the radar echo intensity, and the overhang structure and the horizontal reflectivity gradient. The 10 features are transformed into two‐dimensional comprehensive features by principal component analysis (PCA). The two comprehensive features are named the volume measurement comprehensive feature (VMCF) and the height‐gradient comprehensive feature (HGCF). Through an analysis of 49 hail cases and 35 heavy rainfall cases with S‐band radar data, the time series exhibit a distinct increase in VMCF or HGCF values in the early stage of a hail storm. However, the VMCF and HGCF values of heavy rainfall events remain relatively stable throughout the storm life cycle. An experiment involving real‐storm events, including 31 hail cases and 33 heavy rainfall cases, indicated that the probability of detection of hail storms was 93.33% and the false alarm ratio was 15.66%. In the cases that could be successfully identified as hail storms, 80.00% were detected within 18 min of reaching a hail storm reflectivity of 40 dBZ.