A numerical experiment on hail forecast: Hailstorms on 17 March 2020 in western North Vietnam

Duy Doan Manh; Truong Nguyen Minh; Thu Nguyen Vinh; Thuat Hoang Thi Thanh

doi:10.15625/2615-9783/21329

Author affiliations

Authors

Doan Manh Duy Faculty of Meteorology Hydrology and Oceanography, VNU University of Science
Nguyen Minh Truong
Nguyen Vinh Thu Vietnam National Center for Meteorological and Hydrological Network
Hoang Thi Thanh Thuat Vietnam National Center for Meteorological and Hydrological Network

DOI:

https://doi.org/10.15625/2615-9783/21329

Keywords:

Hail, HSDA, thunderstorm indices, HAILCAST

Abstract

This study utilized the Weather Research and Forecast (WRF) model to forecast hail induced by the hailstorms on 17 March 2020 in western North Vietnam, using two microphysical schemes: the Thompson and Morrison schemes. Assessment of the WRF skill in predicting hail coverage and intensity was done for two predicted indices, namely UH (Updraft Helicity) and CTG (Column Total Integrated Graupel). Two predicted variables are D_Th (hail diameter given by WRF using the Thompson Hail Algorithm) and D_Hc (hail diameter given by the HAILCAST submodel in WRF). The predicted hail coverage and intensity were compared with the products given by the Pha Din radar's Hail Size Discrimination Algorithm (HSDA) for three categories: small, large, and giant hail size. Using the Morrison scheme, the WRF model indicates that the hail-coverage forecast skills of UH, CTG, and D_Hc are highest, with an insignificant difference at the horizontal scale larger than 60 km. However, the D_Hc variable given by the Morrison scheme provides the most successful forecast for both hail size and coverage compared with the HSDA products and field reports. This is because HAILCAST considers kinematic and microphysical processes to predict maximum hail size at the surface. The predicted hailstorms could occur in environments with moderate convective available potential energy but require robust moisture flux convergence over high mountains.

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