Case studies of hailstorms in Shandong Province using hail size discrimination algorithm based on dual polarimetric parameters

Abstract

The hail size discrimination algorithm (HSDA) and its capacity to identify hail in Shandong Province are analyzed to satisfy the localized requirement by China’s S-band dual-polarization radars. A modified HSDA is obtained by using optimized membership function thresholds based on the statistics of Shandong hail data. The results are verified by a supercell storm process. 1) The modified HSDA improves the identification of large hail and giant hail. The results are consistent with the analysis of the scattering and polarization parameter characteristics of different-size hails, the dynamic and microphysical characteristics for supercell, and the real situation. 2) The horizontal and vertical hail-size distribution characteristics are consistent with the analysis about the growth process of larger hails and the precipitation particles filtering mechanisms in supercells. Small hail first forms at the suspension echo, then is injected into the larger hail growth area above the bounded weak echo area driven by updrafts, colliding with the abundant supercooled water in the K_DP column. Finally, large hail and giant hail fall near the direction of the updrafts to form a strong echo wall, and giant hail falls 6–12 km from the central updraft. 3) The maxima of the Z_DR and K_DP columns can be used to predict the hail-growth trend, which exceeds the −20°C isotherm for the heavy-hail growth stage at high-altitude in the supercell storm. When hail falls to the ground, the Z_DR column shortens and the K_DP column disappears, which provides the observation basis from polarimetric radars for the consumption of supercooled water by hail growth.

Graphical abstract

Keywords

polarimetric parameters / hail size discrimination / membership function / giant hail

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Acknowledgments

This study was supported by the Shandong Provincial Natural Science Foundation of China (Nos. ZR2020MD052 and ZR2022MD072), the National Natural Science Foundation of China (Grant No. 41675029), East China Regional Collaborative Innovation Fund for Meteorological Science and Technology (No. QYHZ202101), Project of Xiamen Bureau of Science and Technology (No. 3502Z20214ZD4005), Key Project of Shandong Meteorological Bureau (Nos. 2021SDQXZ09 and SDLD2022-02)

Competing interests

The authors declare that they have no competing interests.

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