Editorial Type:
Article
Article Type:
Research Article

Comparison of Three Radar-Based Precipitation Nowcasts for the Extreme July 2021 Flooding Event in Germany

Mohamed Saadi aInstitute of Bio- and Geosciences (IBG-3, Agrosphere), Forschungszentrum Jülich, Jülich, Germany
bCentre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, Germany
cInstitut de Mécanique des Fluides de Toulouse, Université de Toulouse, CNRS-INPT-UPS, Toulouse, France

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https://orcid.org/0000-0001-8951-3993
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Carina Furusho-Percot aInstitute of Bio- and Geosciences (IBG-3, Agrosphere), Forschungszentrum Jülich, Jülich, Germany
bCentre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, Germany
dU.S. 1116 AGROCLIM, INRAE Centre de Recherche PACA, Avignon, France

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Alexandre Belleflamme aInstitute of Bio- and Geosciences (IBG-3, Agrosphere), Forschungszentrum Jülich, Jülich, Germany
bCentre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, Germany

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Silke Trömel eInstitute for Geosciences, Department of Meteorology, Universität Bonn, Bonn, Germany
fLaboratory for Clouds and Precipitation Exploration, Geoverbund ABC/J, Bonn, Germany

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Stefan Kollet aInstitute of Bio- and Geosciences (IBG-3, Agrosphere), Forschungszentrum Jülich, Jülich, Germany
bCentre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, Germany

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Ricardo Reinoso-Rondinel eInstitute for Geosciences, Department of Meteorology, Universität Bonn, Bonn, Germany
gFaculty of Engineering Science, Department of Civil Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
hDepartment of Meteorological Observations and Research, Royal Meteorological Institute of Belgium, Brussels, Belgium

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Online Publication:
30 Jun 2023
Print Publication:
01 Jul 2023
DOI:
https://doi.org/10.1175/JHM-D-22-0121.1
Page(s):
1241–1261
Restricted access

Abstract

Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts, especially for lead times up to 12 h, 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 spectral prognosis (S-PROG)] and one probabilistic [Short-Term Ensemble Prediction System (STEPS)] QPN with a 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 with 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 with 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.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mohamed Saadi, mohamed.saadi@toulouse-inp.fr

Abstract

Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts, especially for lead times up to 12 h, 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 spectral prognosis (S-PROG)] and one probabilistic [Short-Term Ensemble Prediction System (STEPS)] QPN with a 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 with 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 with 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.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mohamed Saadi, mohamed.saadi@toulouse-inp.fr
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