Articles | Volume 18, issue 16
https://doi.org/10.5194/gmd-18-5291-2025
https://doi.org/10.5194/gmd-18-5291-2025
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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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
    • AC1: 'Reply on RC1', Shaofeng Hua, 13 May 2025
  • RC2: 'Comment on egusphere-2024-3834', Anonymous Referee #2, 02 May 2025
    • 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 
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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.
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