Comparison of three radar-based precipitation nowcasts for the extreme July 2021 flooding event in Germany

IF 3.1 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Mohamed Saadi, C. Furusho‐Percot, Alexandre Belleflamme, S. Trömel, S. Kollet, R. Reinoso-Rondinel
{"title":"Comparison of three radar-based precipitation nowcasts for the extreme July 2021 flooding event in Germany","authors":"Mohamed Saadi, C. Furusho‐Percot, Alexandre Belleflamme, S. Trömel, S. Kollet, R. Reinoso-Rondinel","doi":"10.1175/jhm-d-22-0121.1","DOIUrl":null,"url":null,"abstract":"\nQuantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts especially for lead times up to 12 hours, but their evaluation depends on a variety of factors, namely the choice of the hydrological model and the benchmark. We tested three precipitation nowcasting techniques based on radar observations for the disastrous mid-July 2021 event in seven German catchments (140-1670 km2). Two deterministic (advection-based and S-PROG) and one probabilistic (STEPS) QPN with maximum lead time of 3 h were used as input to two hydrological models: a physically-based, 3D-distributed model (ParFlowCLM) and a conceptual, lumped model (GR4H). We quantified the hydrological added value of QPN compared to hydrological persistence and zero-precipitation nowcasts as benchmarks. For the 14 July 2021 event, we obtained the following key results: (1) According to the quality of the forecasted hydrographs, exploiting QPN improved the lead times by up to 4 h (8 h) compared to adopting zero-precipitation nowcasts (hydrological persistence) as a benchmark. Using a skill-based approach, obtained improvements were up to 7-12 h depending on the benchmark. (2) The three QPN techniques obtained similar performances regardless of the applied hydrological model. (3) Using zero-precipitation nowcasts instead of hydrological persistence as benchmark reduced the added value of QPN. These results highlight the need for combining a skill-based approach with an analysis of the quality of forecasted hydrographs to rigorously estimate the added value of QPN.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrometeorology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/jhm-d-22-0121.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 1

Abstract

Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts especially for lead times up to 12 hours, but their evaluation depends on a variety of factors, namely the choice of the hydrological model and the benchmark. We tested three precipitation nowcasting techniques based on radar observations for the disastrous mid-July 2021 event in seven German catchments (140-1670 km2). Two deterministic (advection-based and S-PROG) and one probabilistic (STEPS) QPN with maximum lead time of 3 h were used as input to two hydrological models: a physically-based, 3D-distributed model (ParFlowCLM) and a conceptual, lumped model (GR4H). We quantified the hydrological added value of QPN compared to hydrological persistence and zero-precipitation nowcasts as benchmarks. For the 14 July 2021 event, we obtained the following key results: (1) According to the quality of the forecasted hydrographs, exploiting QPN improved the lead times by up to 4 h (8 h) compared to adopting zero-precipitation nowcasts (hydrological persistence) as a benchmark. Using a skill-based approach, obtained improvements were up to 7-12 h depending on the benchmark. (2) The three QPN techniques obtained similar performances regardless of the applied hydrological model. (3) Using zero-precipitation nowcasts instead of hydrological persistence as benchmark reduced the added value of QPN. These results highlight the need for combining a skill-based approach with an analysis of the quality of forecasted hydrographs to rigorously estimate the added value of QPN.
2021年7月德国极端洪水事件的三个基于雷达降水临近预报的比较
定量降水临近预报(Quantitative precipitation nowcast, QPN)可以提高洪水预报的准确性,尤其是提前期长达12小时的洪水预报,但其评估取决于多种因素,即水文模型和基准的选择。我们在德国七个集水区(140-1670平方公里)对2021年7月中旬灾难性事件的雷达观测测试了三种降水临近预报技术。两个确定性(基于平流和S-PROG)和一个概率(STEPS) QPN的最大提前时间为3小时,被用作两个水文模型的输入:一个基于物理的3d分布模型(ParFlowCLM)和一个概念性的集总模型(GR4H)。我们量化了QPN的水文附加价值,并将其与水文持久性和零降水临近预报相比较作为基准。对于2021年7月14日的事件,我们获得了以下关键结果:(1)根据预测的水文曲线的质量,与采用零降水临近预报(水文持久性)作为基准相比,利用QPN将提前时间缩短了4小时(8小时)。使用基于技能的方法,根据基准,获得的改进可达7-12小时。(2)无论应用的水文模型如何,三种QPN技术都获得了相似的性能。(3)以零降水临近预报代替水文持续性作为基准,降低了QPN的附加值。这些结果强调需要将基于技能的方法与预测的水文质量分析相结合,以严格估计QPN的附加值。
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 求助全文
来源期刊
Journal of Hydrometeorology
Journal of Hydrometeorology 地学-气象与大气科学
CiteScore
7.40
自引率
5.30%
发文量
116
审稿时长
4-8 weeks
期刊介绍: The Journal of Hydrometeorology (JHM) (ISSN: 1525-755X; eISSN: 1525-7541) publishes research on modeling, observing, and forecasting processes related to fluxes and storage of water and energy, including interactions with the boundary layer and lower atmosphere, and processes related to precipitation, radiation, and other meteorological inputs.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信