Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20 July 2021: Temporal–Spatial Variability and Implications for Radar QPE

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Meteorological Research Pub Date : 2024-07-09 DOI:10.1007/s13351-024-3119-9
Liman Cui, Haoran Li, Aifang Su, Yang Zhang, Xiaona Lyu, Le Xi, Yuanmeng Zhang
{"title":"Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20 July 2021: Temporal–Spatial Variability and Implications for Radar QPE","authors":"Liman Cui, Haoran Li, Aifang Su, Yang Zhang, Xiaona Lyu, Le Xi, Yuanmeng Zhang","doi":"10.1007/s13351-024-3119-9","DOIUrl":null,"url":null,"abstract":"<p>In this study, a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal–spatial variability of raindrop size distributions (DSDs) in the Zhengzhou extreme rainfall event on 20 July 2021. The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement, despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm. The Parsivel OTT observations show prominent temporal–spatial variations of DSDs, and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500–1600 LST (local standard time) was reported. This hourly rainfall is characterized by fairly high concentrations of large raindrops, and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h<sup>−1</sup>. Besides, polarimetric radar observations show the highest differential phase shift (<i>K</i><sub>dp</sub>) and differential reflectivity (<i>Z</i><sub>dr</sub>) near surface over Zhengzhou Station from 1500 to 1600 LST. In light of the remarkable temporal–spatial variability of DSDs, a reflectivity-grouped fitting approach is proposed to optimize the reflectivity–rain rate (<i>Z–R</i>) parameterization for radar quantitative precipitation estimation (QPE), and the rain gauge measurements are used for validation. The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h<sup>−1</sup>, as compared with the fixed <i>Z–R</i> parameterization. This study reveals the drastic temporal–spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting <i>Z–R</i> relationships for radar QPE of such events.</p>","PeriodicalId":48796,"journal":{"name":"Journal of Meteorological Research","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Meteorological Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s13351-024-3119-9","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

In this study, a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal–spatial variability of raindrop size distributions (DSDs) in the Zhengzhou extreme rainfall event on 20 July 2021. The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement, despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm. The Parsivel OTT observations show prominent temporal–spatial variations of DSDs, and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500–1600 LST (local standard time) was reported. This hourly rainfall is characterized by fairly high concentrations of large raindrops, and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h−1. Besides, polarimetric radar observations show the highest differential phase shift (Kdp) and differential reflectivity (Zdr) near surface over Zhengzhou Station from 1500 to 1600 LST. In light of the remarkable temporal–spatial variability of DSDs, a reflectivity-grouped fitting approach is proposed to optimize the reflectivity–rain rate (Z–R) parameterization for radar quantitative precipitation estimation (QPE), and the rain gauge measurements are used for validation. The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h−1, as compared with the fixed Z–R parameterization. This study reveals the drastic temporal–spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z–R relationships for radar QPE of such events.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
2021 年 7 月 20 日郑州极端降雨事件中的雨滴粒径分布:时空变异性及其对雷达 QPE 的影响
本研究利用覆盖郑州市区及邻近地区的区域 Parsivel OTT 测距仪网络,研究了 2021 年 7 月 20 日郑州极端降雨事件中雨滴粒径分布的时空变异性。尽管 24 小时累计降雨量在 198.3 毫米到 624.1 毫米之间存在显著的站点间差异,但 6 个运行站点的测距仪和雨量计观测到的降雨率非常一致。Parsivel OTT 的观测结果显示,DSD 的时空变化非常明显,其中郑州站的变化最为剧烈,当地标准时间 15 时至 16 时的每小时降雨量达到 201.9 毫米,创下历史新高。这种小时降雨的特点是大雨滴相当集中,质量加权雨滴直径一般随降雨率增加而增大,然后在降雨率约为 50 毫米/小时-1 时达到降雨量分布的平衡状态。此外,偏振雷达观测结果表明,郑州站近地面的差分相移(Kdp)和差分反射率(Zdr)在当地时间 15 时至 16 时最高。针对差分相移显著的时空变异性,提出了一种反射率分组拟合方法,以优化雷达定量降水估算(QPE)的反射率-雨量(Z-R)参数化,并利用雨量计实测数据进行验证。结果表明,与固定的 Z-R 参数化相比,当降雨强度超过 20.0 mm h-1 时,平均偏差比从 0.57 增加到 0.79,均方根误差从 23.69 减小到 18.36。这项研究揭示了郑州极端降雨事件中雨微物理的剧烈时空变化,并证明了在此类事件的雷达 QPE 中使用反射率分组拟合 Z-R 关系的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Meteorological Research
Journal of Meteorological Research METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
6.20
自引率
6.20%
发文量
54
期刊介绍: Journal of Meteorological Research (previously known as Acta Meteorologica Sinica) publishes the latest achievements and developments in the field of atmospheric sciences. Coverage is broad, including topics such as pure and applied meteorology; climatology and climate change; marine meteorology; atmospheric physics and chemistry; cloud physics and weather modification; numerical weather prediction; data assimilation; atmospheric sounding and remote sensing; atmospheric environment and air pollution; radar and satellite meteorology; agricultural and forest meteorology and more.
期刊最新文献
Precipitation Evolution from Plain to Mountains during the July 2023 Extreme Heavy Rainfall Event in North China Enhancing Tropical Cyclone Intensity Estimation from Satellite Imagery through Deep Learning Techniques Ground Passive Microwave Remote Sensing of Atmospheric Profiles Using WRF Simulations and Machine Learning Techniques MGCPN: An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data GOES-16 ABI Brightness Temperature Observations Capturing Vortex Rossby Wave Signals during Rapid Intensification of Hurricane Irma (2017)
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1