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https://doi.org/10.5194/egusphere-2024-3834
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2024-3834
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Feb 2025
| 12 Feb 2025
Mitigating Hail Overforecasting in the 2-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)
Abstract. Hail forecasting using numerical models remains a challenge due to the uncertainties and deficiencies in microphysics schemes. In this study, we assessed the hail simulation performance of the 2-moment Milbrandt-Yau (MY2) microphysics scheme within the Weather Research and Forecast (WRF) model by simulating three heavy rainfall events in Meiyu systems in which hail was rarely observed, rather than focusing on hail cases as done in previous researches. Simulation results showed that MY2 scheme produced noticeable hail in these rainstorms. Further analysis revealed that the overprediction of hail was caused by the imperfect graupel-to-hail conversion parameterization method adopted in the MY2 scheme. By incorporating the graupel spongy wet growth process, the modified scheme significantly mitigated the hail overforecasting. Moreover, the modified MY2 scheme kept the ability to simulate hail in real hail cases, demonstrating its ability to differentiate between heavy rainfall and hail events—a distinction the original scheme lacked. By comparing simulations of both rainstorms and hailstorms, it is concluded that the upward transport of large raindrops near the 0 °C level is critical for the graupel-to-hail conversion.
How to cite. Hua, S., Chen, G., Chen, B., Li, M., and Xu, X.: Mitigating Hail Overforecasting in the 2-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), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3834, 2025.
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Journal article(s) based on this preprint
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
Geosci. Model Dev., 18, 5291–5309, https://doi.org/10.5194/gmd-18-5291-2025, https://doi.org/10.5194/gmd-18-5291-2025, 2025
Short summary
Short summary
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.
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2024-3834', Anonymous Referee #1, 16 Mar 2025
- Detailed comparisons of the simulations against radar observations have been conducted for hail presence within storms using both the original and modified schemes. However, it is also crucial to examine the corresponding surface hail distribution for both rain and hail events, as accurate surface hail prediction, including hail size and amounts are vital for operational weather forecasting.
- According to dual-polarization radar observations using the HCA, no hail was identified throughout the entire lifespan of the three heavy rainstorms. These findings contradict the simulated rainstorms, which showed a large amount of hail particles even with the modified Milbrandt scheme. Please provide further details on this inconsistency.
- The graupel-to-hail conversion in the revised scheme requires a substantial amount of supercooled water collected by graupel at one model time step, specifically at least 1.25 times the mass of graupel particles. This criterion may be too stringent, which explains why it mitigates hail overprediction within rainstorms. However, while hail can be produced within the hailstorms, the simulated radar reflectivity and hail distributions are underestimated compared to radar observations. Additionally, the integration time step also influences the graupel-to-hail conversion, which is also not physically reasonable.
- The discussions in the last two paragraphs of Section 3 lack clarity. For instance, while both thermal buoyancy and dynamic vertical pressure gradient force are important for the vertical motion, only the buoyancy term is investigated.
Citation: https://doi.org/10.5194/egusphere-2024-3834-RC1 - AC1: 'Reply on RC1', Shaofeng Hua, 13 May 2025
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RC2: 'Comment on egusphere-2024-3834', Anonymous Referee #2, 02 May 2025
My comments are documented in the attached pdf file.
- AC2: 'Reply on RC2', Shaofeng Hua, 13 May 2025
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2024-3834', Anonymous Referee #1, 16 Mar 2025
- Detailed comparisons of the simulations against radar observations have been conducted for hail presence within storms using both the original and modified schemes. However, it is also crucial to examine the corresponding surface hail distribution for both rain and hail events, as accurate surface hail prediction, including hail size and amounts are vital for operational weather forecasting.
- According to dual-polarization radar observations using the HCA, no hail was identified throughout the entire lifespan of the three heavy rainstorms. These findings contradict the simulated rainstorms, which showed a large amount of hail particles even with the modified Milbrandt scheme. Please provide further details on this inconsistency.
- The graupel-to-hail conversion in the revised scheme requires a substantial amount of supercooled water collected by graupel at one model time step, specifically at least 1.25 times the mass of graupel particles. This criterion may be too stringent, which explains why it mitigates hail overprediction within rainstorms. However, while hail can be produced within the hailstorms, the simulated radar reflectivity and hail distributions are underestimated compared to radar observations. Additionally, the integration time step also influences the graupel-to-hail conversion, which is also not physically reasonable.
- The discussions in the last two paragraphs of Section 3 lack clarity. For instance, while both thermal buoyancy and dynamic vertical pressure gradient force are important for the vertical motion, only the buoyancy term is investigated.
Citation: https://doi.org/10.5194/egusphere-2024-3834-RC1 - AC1: 'Reply on RC1', Shaofeng Hua, 13 May 2025
-
RC2: 'Comment on egusphere-2024-3834', Anonymous Referee #2, 02 May 2025
My comments are documented in the attached pdf file.
- AC2: 'Reply on RC2', Shaofeng Hua, 13 May 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Shaofeng Hua on behalf of the Authors (13 May 2025)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (19 May 2025) by Holger Tost
RR by Anonymous Referee #1 (20 May 2025)
ED: Publish as is (02 Jun 2025) by Holger Tost
AR by Shaofeng Hua on behalf of the Authors (04 Jun 2025)
Manuscript
Journal article(s) based on this preprint
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
Geosci. Model Dev., 18, 5291–5309, https://doi.org/10.5194/gmd-18-5291-2025, https://doi.org/10.5194/gmd-18-5291-2025, 2025
Short summary
Short summary
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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Shaofeng Hua
China Meteorological Adminstration Key Laboratory of Cloud-Precipitation Physics and Weather Modification, Beijing, 100081, China
China Meteorological Adminstration Weather Modification Centre, Beijing, 100081, China
Gang Chen
CORRESPONDING AUTHOR
Nanjing Joint Institute for Atmospheric Sciences, Nanjing, 210009, China
Baojun Chen
China Meteorological Adminstration Key Laboratory of Cloud-Precipitation Physics and Weather Modification, Beijing, 100081, China
China Meteorological Adminstration Weather Modification Centre, Beijing, 100081, China
Mingshan Li
Jingmen Meteorological Service, Jingmen, 448000, China
Key Laboratory of Mesoscale Severe Weather/Ministry of Education, and School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
CMA Radar Meteorology Key Laboratory, Nanjing, 210023, China
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Short summary
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.
Hail forecasting using numerical models remains a challenge. In this study, we found that the...