TY - JOUR
T1 - An investigation into the dominant cloud microphysical processes in extreme-rain-producing storms occurred on 7 May 2017 over Southern China
AU - Yin, Jinfang
AU - Wang, Liyan
AU - Li, Feng
AU - Li, Haoran
AU - Zhou, Zhiming
AU - Wang, Hong
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2025/3
Y1 - 2025/3
N2 - This paper presents an analysis of the dominant cloud microphysical processes of the extreme rainfall event on 7 May 2017, using a series of convective-permitting simulations. Special emphasis is placed on the microphysical processes of two extreme-rain-producing storms, yielding hourly rainfalls exceeding 120 mm. For the Huashan (HS) storm, a large amount of cloud water is produced through condensation (PRW_VCD) within the storm, and significant rainwater is generated by the collection of cloud water by raindrops (PRR_RCW). As for the Jiulong (JL) storm, warm rain microphysical processes are as same as the HS storm. Additionally, considerable rainwater is produced via the collection of graupel by raindrops (PRR_RCG), with contributions also coming from the melting of graupel (PRR_GML). It is noteworthy that there is slight evaporation of raindrops (PRV_REV) in both storms. To verify the dominant cloud microphysical processes of the extreme rainfalls, an experiment has been conducted using a simple ice microphysics scheme that covers the aforementioned dominant microphysical processes. The results indicate that extreme rainfalls are well replicated with the simple microphysics scheme, showing good agreement in spatial distribution and temporal evolution with observations and the control run. The experiment confirms largely the dominant cloud microphysical processes responsible for the extreme rainfall. Based on the results, we propose that placing special emphasis on the treatment of snow terminal velocity in the Thompson scheme would improve the performance of the scheme for heavy rainfall simulation. The findings gained here may help further understand cloud microphysical processes for localized extreme rainfall over southern China, and provide guidance for the improvement of cloud microphysics schemes.
AB - This paper presents an analysis of the dominant cloud microphysical processes of the extreme rainfall event on 7 May 2017, using a series of convective-permitting simulations. Special emphasis is placed on the microphysical processes of two extreme-rain-producing storms, yielding hourly rainfalls exceeding 120 mm. For the Huashan (HS) storm, a large amount of cloud water is produced through condensation (PRW_VCD) within the storm, and significant rainwater is generated by the collection of cloud water by raindrops (PRR_RCW). As for the Jiulong (JL) storm, warm rain microphysical processes are as same as the HS storm. Additionally, considerable rainwater is produced via the collection of graupel by raindrops (PRR_RCG), with contributions also coming from the melting of graupel (PRR_GML). It is noteworthy that there is slight evaporation of raindrops (PRV_REV) in both storms. To verify the dominant cloud microphysical processes of the extreme rainfalls, an experiment has been conducted using a simple ice microphysics scheme that covers the aforementioned dominant microphysical processes. The results indicate that extreme rainfalls are well replicated with the simple microphysics scheme, showing good agreement in spatial distribution and temporal evolution with observations and the control run. The experiment confirms largely the dominant cloud microphysical processes responsible for the extreme rainfall. Based on the results, we propose that placing special emphasis on the treatment of snow terminal velocity in the Thompson scheme would improve the performance of the scheme for heavy rainfall simulation. The findings gained here may help further understand cloud microphysical processes for localized extreme rainfall over southern China, and provide guidance for the improvement of cloud microphysics schemes.
KW - Collection of cloud water by raindrops
KW - Dominant cloud microphysical processes
KW - Dynamically track
KW - Extreme rainfall
KW - Water vapor condensation
UR - http://www.scopus.com/inward/record.url?scp=85210302673&partnerID=8YFLogxK
U2 - 10.1016/j.atmosres.2024.107820
DO - 10.1016/j.atmosres.2024.107820
M3 - Article
AN - SCOPUS:85210302673
SN - 0169-8095
VL - 314
JO - Atmospheric Research
JF - Atmospheric Research
M1 - 107820
ER -