Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 16
Articles | Volume 18, issue 16
https://doi.org/10.5194/gmd-18-5291-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-5291-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 16
Model evaluation paper
 | 
26 Aug 2025
Model evaluation paper |  | 26 Aug 2025

Mitigating hail overforecasting in the two-moment Milbrandt–Yau microphysics scheme (v2.25.2_beta_04) in WRF (v4.5.1) by incorporating the graupel spongy wet growth process (MY2_GSWG v1.0)

Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu

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Cited articles

Adams-Selin, R. D. and Ziegler, C. L.: Forecasting hail using a one-dimensional hail growth model within WRF, Mon. Weather Rev., 144, 4919–4939, https://doi.org/10.1175/MWR-D16-0027.1, 2016. 
Adams-Selin, R. D., Clark, A. J., Melick, C. J., Dembek, S. R., Jirak, I. L., and Ziegler, C. L.: Evolution of WRF-HAILCAST during the 2014–16 NOAA/Hazardous Weather Testbed Spring Forecasting Experiments, Weather Forecast., 34, 61–79, https://doi.org/10.1175/WAF-D-18-0024.1, 2019. 
Adams-Selin, R. D., Kalb, C., Jensen, T., Henderson, J., Supinie, T., Harris, L., Wang Y. H., Gallo B. T., and Clark A. J.: Just What Is “Good”? Musings on Hail Forecast Verification through Evaluation of FV3-HAILCAST Hail Forecasts, Weather Forecast., 38, 371–387, https://doi.org/10.1175/WAF-D-22-0087.1, 2023. 
Allen, J., Karoly, D., and Mills, G.: A severe thunderstorm climatology for Australia and associated thunderstorm environments, Aust. Meteorol. Oceanogr. J., 61, 143–158, https://doi.org/10.22499/2.6103.001, 2011. 
Allen, J. T., Giammanco, I. M., Kumjian, M. R., Punge, H. J., Zhang, Q., Groenemeijer, P., Kunz, M., and Ortega, K.: Understanding Hail in the Earth System, Rev. Geophys., 58, e2019RG000665, https://doi.org/10.1029/2019RG000665, 2019. 
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
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