Near-Global Three-Dimensional Hail Signals Detected by Using GPM-DPR Observations

ournal of the Meteorological Society of Japan

article

Tatsuya SEIKI¹ 

[1] Japan Agency for Marine-Earth Science and Technology

关键词: Global Precipitation Measurement;    satellite observation;    hail;    ice cloud microphysics;    severe storm;   

DOI  :  10.2151/jmsj.2021-018

来源: Meteorological Society of Japan

【 摘 要 】

This study proposes a method of detecting three-dimensional hail distribution by using the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) products in combination with the atmospheric temperature from a reanalysis product. In this study, the hail class contains hailstones, high-density graupel, and small frozen droplets. The radar reflectivity at the Ku-band (ZKu) and dual-frequency ratio (DFR) values are examined for hydrometeor classification at the five atmospheric temperature ranges in comparison to the ground radar product in a test hailstorm case. A simple model assuming binary collision for the riming process, which represents a significant reduction in the number concentration with the conservation of the mass concentration, explains well the hail signals on the scatterplot of ZKu and the DFR. This study determines the thresholds of the ZKu and DFR values at each temperature range based on the simple model. Furthermore, this study evaluates the thresholds in 74 hailstorm cases and proposes two filters to remove melting snow and rain contamination below the freezing level. The 5-year dataset of the GPM-DPR observations shows that hail is widely distributed over oceanic convergence zones as well as over continental convective regions. Most oceanic hail layers are found to be thin (i.e., less than 1500 m) and confined near the freezing level. Therefore, such hail signals have been potentially missed by ground observations. An additional filter removes such thin hail layers and effectively works to detect only deep hailstorms, specifically over continental regions.

【 授权许可】

Unknown