Forecast of the hailstorm event on March 21, 2021 in the Northwest region of Vietnam using numerical model
Main Article Content
Abstract
This study uses the WRF model to forecast the hailstorm event of March 21, 2021 in the Northwestern provinces of Vietnam, based on three hail size prediction indices: the Maximum Estimated Size of Hail from simulated reflectivity (MESH_WRF), the Thompson algorithm (Dmax), and the WRF-Hailcast model (HC). Using two microphysics schemes, Milbrandt–Yau (MY2) and Morrison (MOR), the model was able to forecast the development of the hail-producing convective system, in terms of both location and intensity. When evaluated against radar-derived MESH data, the Dmax index from MY2 showed the highest skill in predicting hail occurrence area. Both MESH_WRF products overforecasted the hail area, while HC had almost no predictive capability for this event. Microphysical analysis of the largest convective cell revealed that the hail phase in MY2 had a lower distribution and larger mean size at low levels compared to the graupel phase in MOR. As a result, the MESH_WRF and Dmax products from MY2 provided better forecasts than those from MOR.
Keywords: Hail, MESH, WRF, Microphysics
References
[21] Voice of Vietnam (VOV). (2020, March 23). Large hail in Điện Biên causes damage to houses and crops. Voice of Vietnam Online Newspaper, https://vov.vn/xa-hoi/mua-da-lon-tai-dien-bien-gay-thiet-hai-ve-nha-cua-hoa-mau-844814.vov, (Accessed 15 August 2025)
[22] Vietnam disaster and dyke management authority. (2021, March 22). Rapid report of the duty shift on natural disaster prevention and control, https://phongchongthientai.mard.gov.vn/Pages/bao-cao-nhanh-truc-ban-cong-tac-pctt-ngay-22-3-2021.aspx, (Accessed 15 August 2025)
[23] Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, Z. Liu, J. Berner, W. Wang, J. G. Powers, M. G. Duda, D. M. Barker, and X.-Y. Huang, 2019: A Description of the Advanced Research WRF Version 4. NCAR Tech. Note NCAR/TN-556+STR, 145 pp.
doi:10.5065/1dfh-6p97
[24] National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. (2015). NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory.
https://doi.org/10.5065/D65D8PWK
[25] Morrison, H., Curry, J. A., & Khvorostyanov, V. I. (2005). A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description. Journal of the Atmospheric Sciences, 62(6), 1665-1677. https://doi.org/10.1175/JAS3446.1
[26] Witt, A., Eilts, M. D., Stumpf, G. J., Johnson, J. T., Mitchell, E. D., & Thomas, K. W. (1998). An enhanced hail detection algorithm for the WSR-88D. Weather and Forecasting, 13(2), 286-303
[27] Murillo, E. M., Homeyer, C. R., & Allen, J. T. (2021). A 23-Year severe hail climatology using GridRad MESH observations. Monthly Weather Review, 149(4), 945-958. https://doi.org/10.1175/MWR-D-20-0178.1
[28] Roberts N.M., Lean H.W., 2008. Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events.
Monthly Weather Review, 136(1), 78–97. https://doi.org/10.1175/2007MWR2123.1
[29] Labriola J., N. Snook, Y. Jung, M. Xue, 2019b. Explicit Ensemble Prediction of Hail in 19 May 2013 Oklahoma City Thunderstorms and Analysis of Hail Growth Processes with Several Multimoment Microphysics Schemes. Mon. Wea. Rev.,
147, 1193–1213. https://doi.org/10.1175/MWR-D-18-0266.1.
[30] Skok, G. and Roberts, N. (2018), Estimating the displacement in precipitation forecasts using the Fractions Skill Score. Q.J.R. Meteorol. Soc., 144: 414-425. https://doi.org/10.1002/qj.3212