Improving usability of weather radar data in environmental sciences : potentials, challenges, uncertainties and applications

Precipitation is a crucial driver for many environmental processes and exhibits a high spatiotemporal variability. The traditional, widely-used point-scale measurements by rain gauges are not able to detect the spatial rainfall distribution in a comprehensive way. Throughout the last decades, weather radars have emerged as a new measurement technique that is capable of providing areal precipitation information with high spatial and temporal resolution and put precipitation monitoring on a new level. However, radar is an indirect remote sensing technique. Rain rates and distributions are inferred from measured reflectivities, which are subject to a series of potential error sources. In the last years, several operational national radar data archives exceeded a time series length of ten years and several new radar climatology datasets have been derived, which provide largely consistent, well-documented radar quantitative precipitation estimate (QPE) products and open up new climatological application fields for radar data. However, beside uncertainties regarding data quality and precipitation quantification, several technical barriers exist that can prevent potential users from working with radar data. Challenges include for instance different proprietary data formats, the processing of large data volumes and a scarcity of easy-to-use and free-of-charge software, additional effort for data quality evaluation and difficulties concerning data georeferencing. This thesis provides a contribution to improve the usability of radar-based QPE products, to raise awareness on their potentials and uncertainties and to bridge the gap between the radar community and other scientific disciplines which are still rather reluctant to use these highly resolved data. First, a GIS-compatible Python package was developed to facilitate weather radar data processing. The package uses an efficient workflow based on widely used tools and data structures to automate raw data processing and data clipping for the operational German radar-based and gauge-adjusted QPE called RADOLAN (“RADar OnLine Aneichung”) and the reanalysed radar climatology dataset named RADKLIM. Moreover, the package provides functions for temporal aggregation, heavy rainfall detection and data exchange with ArcGIS. The Python package was published as an Open Source Software called radproc. It was used as a basis for all subsequent analyses conducted in this study and has already been applied successfully by several scientific working groups and students conducting heavy rainfall analysis and data aggregation tasks. Second, this study explored the development, uncertainties and potentials of the hourly RADOLAN and RADKLIM QPE products in comparison to ground-truth rain gauge data. Results revealed that both QPE products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, the analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation and range-dependent attenuation. The applicability of the evaluation results was underpinned by the publication of a rainfall inter-comparison geodataset for the RADOLAN, RADKLIM and rain gauge datasets. The intercomparison dataset is a collection of precipitation statistics and several parameters that can potentially influence radar data quality. It allows for a straightforward comparison and analysis of the different precipitation datasets and can support a user’s decision on which dataset is best suited for the given application and study area. The data processing workflow for the derivation of the intercomparison dataset is described in detail and can serve as a guideline for individual data processing tasks and as a case study for the application of the radproc library. Finally, in a case study on radar composite data application for rainfall erosivity estimation, RADKLIM data with a 5-minute temporal resolution were used alongside rain gauge data to compare different erosivity estimation methods used in erosion control practice. The aim was to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Moreover, correction factors proposed in other studies were tested with regard to their ability to compensate for different temporal resolutions of rainfall input data and the underestimation of precipitation by radar data. The results clearly showed that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. The radar climatology data showed a high potential to improve rainfall erosivity estimations, but also a certain bias in the spatial distribution of the R-factor due to the rather short time series and a few radar artefacts. The application of correction factors to compensate for the underestimation of the radar led to an improvement of the results, but a possible overcorrection could not be excluded, which indicated a need for further research on data correction approaches. This thesis concludes with a discussion of the role of open source software, open data and of the implementation of the FAIR (Findable, Accessible, Interoperable, Re-usable) principles for the German radar QPE products in order to improve data usability. Finally, practical recommendations on how to approach the assessment of QPE quality in a specific study area are provided and potential future research developments are pointed out.Niederschlag ist ein wesentlicher Antrieb vieler Umweltprozesse und weist eine hohe räumliche und zeitliche Variabilität auf. Die traditionellen, weit verbreiteten punktuellen Messungen mit Ombrometern sind nicht in der Lage, die räumliche Niederschlagsverteilung flächendeckend zu erfassen. Im Laufe der letzten Jahrzehnte hat sich mit dem Wetterradar eine neue Messtechnik etabliert, die in der Lage ist, flächenhafte Niederschlagsinformationen mit hoher räumlicher und zeitlicher Auflösung zu erfassen und die Niederschlagsüberwachung auf ein neues Niveau zu heben. Radar ist jedoch eine indirekte Fernerkundungstechnik. Niederschlagsraten und -verteilungen werden aus gemessenen Reflektivitäten abgeleitet, die einer Reihe von potenziellen Fehlerquellen unterliegen. In den letzten Jahren überschritten mehrere nationale Radardatenarchive eine Zeitreihenlänge von zehn Jahren. Es wurden mehrere neue Radarklimatologie-Datensätze abgeleitet, die weitgehend konsistente, gut dokumentierte Radarprodukte zur quantitativen Niederschlagsschätzung liefern und neue klimatologische Anwendungsfelder für Radardaten eröffnen. Neben Unsicherheiten bezüglich der Datenqualität und der Niederschlagsquantifizierung gibt es jedoch eine Vielzahl technischer Barrieren, die potenzielle Nutzer von der Verwendung der Radardaten abhalten können. Zu den Herausforderungen gehören beispielsweise unterschiedliche proprietäre Datenformate, die Verarbeitung großer Datenmengen, ein Mangel an einfach zu bedienender und kostenloser Software, zusätzlicher Aufwand für die Bewertung der Datenqualität und Schwierigkeiten bei der Georeferenzierung der Daten. Diese Dissertation liefert einen Beitrag zur Verbesserung der Nutzbarkeit radarbasierter quantitativer Niederschlagsschätzungen, zur Sensibilisierung für deren Potenziale und Unsicherheiten und zur Überbrückung der Kluft zwischen der Radar-Community und anderen wissenschaftlichen Disziplinen, die der Nutzung der Daten immer noch eher zögerlich gegenüberstehen. Zunächst wurde eine GIS-kompatible Python-Bibliothek entwickelt, um die Verarbeitung von Wetterradardaten zu erleichtern. Die Bibliothek verwendet einen effizienten Workflow, der auf weit verbreiteten Werkzeugen und Datenstrukturen basiert, um die Rohdatenverarbeitung und das Zuschneiden der Daten zu automatisieren. Alle Routinen wurden für die operationellen deutschen RADOLAN-Kompositprodukte (“RADar OnLine Aneichung”) und den reanalysierten Radarklimatologie-Datensatz (RADKLIM) umgesetzt. Darüber hinaus bietet das Paket Funktionen für die zeitliche Datenaggregation, die Identifikation und Zählung von Starkregen sowie den Datenaustausch mit ArcGIS. Das Python-Paket wurde als Open-Source-Software namens radproc veröffentlicht. Radproc bildet die methodische Grundlage für alle nachfolgenden Analysen dieser Studie und wurde zudem bereits erfolgreich von mehreren wissenschaftlichen Arbeitsgruppen und Studenten zur Analyse von Starkregen und zeitlichen Aggregierung von Radardaten eingesetzt. Des Weiteren wurden in dieser Arbeit die Entwicklung, Unsicherheiten und Potentiale der stündlichen RADOLAN- und RADKLIM-Kompositprodukte im Vergleich zu Ombrometerdaten analysiert. Die Ergebnisse haben gezeigt, dass beide Radarprodukte die Gesamtniederschlagssummen und inbesondere Niederschläge hoher Intensität tendenziell unterschätzen. Die Analysen zeigten jedoch auch signifikante Verbesserungen im Verlauf der RADOLAN-Zeitreihe sowie deutliche Qualitätsverbesserungen durch die klimatologische Reanalyse, insbesondere im Hinblick auf die Korrektur typischer Radarartefakte, orographischer und winterlicher Niederschläge sowie der entfernungsabhängigen Abschwächung des Radarsignals. Die Anwendbarkeit der Auswertungsergebnisse wurde durch die Veröffentlichung eines Geodatensatzes zum Niederschlagsvergleich für die RADOLAN-, RADKLIM- und Ombrometer-Datensätze untermauert. Der Vergleichsdatensatz ist eine Sammlung von Niederschlagsstatistiken sowie verschiedener Parameter, die die Qualität der Radardaten potenziell beeinflussen können. Er ermöglicht einen einfachen Vergleich und eine Analyse der verschiedenen Niederschlagsdatensätze und kann die Entscheidung von Anwendern unterstützen, welcher Niederschlagsdatensatz für die jeweilige Anwendung und das jeweilige Untersuchungsgebiet am besten geeignet ist. Der Workflow für die Ableitung des Vergleichsdatensatzes wurde ausführlich beschrieben und kann als Leitfaden für individuelle Datenverarbeitungsaufgaben und als Fallstudie für die Anwendung der radproc-Bibliothek dienen. Darüber hinaus wurde eine Fallstudie zur Anwendung von Radar-Komposits für die Abschätzung der Erosivität des Niederschlags durchgeführt. Dazu wurden RADKLIM-Daten und Ombrometerdaten mit einer zeitlichen Auflösung von 5 Minuten verwendet, um verschiedene Methoden zur Abschätzung der Niederschlagserosivität zu vergleichen, die in der Erosionsschutzpraxis Anwendung finden. Ziel war es, die Auswirkungen der Methodik und des Klimawandels sowie der Auflösung, Qualität und der räumlichen Ausdehnung der Eingabedaten auf den R-Faktor der Allgemeinen Bodenabtragsgleichung zu bewerten. Darüber hinaus wurden von anderen Studien vorgeschlagene Korrekturfaktoren im Hinblick auf ihre Fähigkeit getestet, unterschiedliche zeitliche Auflösungen von Niederschlagsdaten und die Unterschätzung des Niederschlags durch Radardaten zu kompensieren. Die Ergebnisse haben deutlich gezeigt, dass die R-Faktoren aufgrund des Klimawandels erheblich zugenommen haben und dass die aktuellen R-Faktor-Karten unter Verwendung neuerer, flächendeckender und räumlich höher aufgelöster Niederschlagsdaten aktualisiert werden müssen. Die Radarklimatologiedaten zeigten ein hohes Potenzial zur Verbesserung der Abschätzung der Niederschlagserosivität, aber aufgrund der vergleichsweise kurzen Zeitreihe und einiger Radarartefakte auch gewisse Unsicherheiten in der räumlichen Verteilung des R-Faktors. Die Anwendung von Korrekturfaktoren zur Kompensation der Unterschätzung des Radars führte zu einer Verbesserung der Ergebnisse, allerdings konnte eine mögliche Überkorrektur nicht ausgeschlossen werden, wodurch weiterer Forschungsbedarf bezüglich der Datenkorrektur aufgezeigt wurde. Diese Arbeit schließt mit einer Diskussion der Rolle von Open-Source-Software, frei verfügbarer Daten und der Umsetzung der FAIR-Prinzipien (Findable, Accessible, Interoperable, Re-usable) für die deutschen Radar-Produkte zur Verbesserung der Nutzbarkeit von Radarniederschlagsdaten. Abschließend werden praktische Empfehlungen zur Vorgehensweise bei der Bewertung der Qualität radarbasierter quantitativer Niederschlagsschätzungen in einem bestimmten Untersuchungsgebiet gegeben und mögliche zukünftige Forschungsentwicklungen aufgezeigt

A rainfall data intercomparison dataset of RADKLIM, RADOLAN, and rain gauge data for Germany

Quantitative precipitation estimates (QPE) derived from weather radars provide spatially and temporally highly resolved rainfall data. However, they are also subject to systematic and random bias and various potential uncertainties and therefore require thorough quality checks before usage. The dataset described in this paper is a collection of precipitation statistics calculated from the hourly nationwide German RADKLIM and RADOLAN QPEs provided by the German Weather Service (Deutscher Wetterdienst (DWD)), which were combined with rainfall statistics derived from rain gauge data for intercomparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The resulting two point shapefiles are readable with all common GIS and constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 to 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, the publication of this data collection has the potential to benefit other users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing

Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales

Study region: The study region is Germany and two sub-regions in Germany, i.e. the state of Rhineland-Palatinate and the city of Reutlingen. Study focus: Opportunistic rainfall sensors, namely personal weather stations and commercial microwave links, together with rain gauge data from the German Weather Service, were used in different combinations to derive rainfall maps with a geostatistical interpolation framework for Germany. This kriging type framework considered the uncertainty of opportunistic sensors and the line structure of commercial microwave links. The resulting rainfall maps were compared to two gauge-adjusted radar products and evaluated to three reference gauge datasets in the respective study regions on both daily and hourly basis. New Hydrological Insights for the Region: The interpolated rainfall products from opportunistic sensors provided good agreement to the reference rain gauges. The dataset combinations including information from the opportunistic sensors performed best. The addition of rain gauges from the German Weather Service did not consistently lead to an improvement of the interpolated rainfall maps. On the country-wide, daily scale the interpolated rainfall maps performed well, but the gauge-adjusted radar products were closer to the reference. For the regional and local scale in Rhineland-Palatinate and Reutlingen with an hourly resolution, the interpolated rainfall maps outperformed the interpolated product from DWD rain gauges and showed a similar agreement to the reference as the radar products

Modelling precipitation intensities from x-band radar measurements using Artificial Neural Networks — a feasibility study for the Bavarian Oberland region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area

Extreme precipitation is a weather phenomenon with tremendous damaging potential for property and human life. As the intensity and frequency of such events is projected to increase in a warming climate, there is an urgent need to advance the existing knowledge on extreme precipitation processes, statistics and impacts across scales. To this end, a working group within the Germany-based project, ClimXtreme, has been established to carry out multidisciplinary analyses of high-impact events. In this work, we provide a comprehensive assessment of the 29 June 2017 heavy precipitation event (HPE) affecting the Berlin metropolitan region (Germany), from the meteorological, impacts and climate perspectives, including climate change attribution. Our analysis showed that this event occurred under the influence of a mid-tropospheric trough over western Europe and two shortwave surface lows over Britain and Poland (Rasmund and Rasmund II), inducing relevant low-level wind convergence along the German–Polish border. Over 11 000 convective cells were triggered, starting early morning 29 June, displacing northwards slowly under the influence of a weak tropospheric flow (10 m s−1 at 500 hPa). The quasi-stationary situation led to totals up to 196 mm d−1, making this event the 29 June most severe in the 1951–2021 climatology, ranked by means of a precipitation-based index. Regarding impacts, it incurred the largest insured losses in the period 2002 to 2017 (EUR 60 million) in the greater Berlin area. We provide further insights on flood attributes (inundation, depth, duration) based on a unique household-level survey data set. The major moisture source for this event was the Alpine–Slovenian region (63 % of identified sources) due to recycling of precipitation falling over that region 1 d earlier. Implementing three different generalised extreme value (GEV) models, we quantified the return periods for this case to be above 100 years for daily aggregated precipitation, and up to 100 and 10 years for 8 and 1 h aggregations, respectively. The conditional attribution demonstrated that warming since the pre-industrial era caused a small but significant increase of 4 % in total precipitation and 10 % for extreme intensities. The possibility that not just greenhouse-gas-induced warming, but also anthropogenic aerosols affected the intensity of precipitation is investigated through aerosol sensitivity experiments. Our multi-disciplinary approach allowed us to relate interconnected aspects of extreme precipitation. For instance, the link between the unique meteorological conditions of this case and its very large return periods, or the extent to which it is attributable to already-observed anthropogenic climate change.</p

Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area

Extreme precipitation is a weather phenomenon with tremendous damaging potential for property and human life. As the intensity and frequency of such events is projected to increase in a warming climate, there is an urgent need to advance the existing knowledge on extreme precipitation processes, statistics and impacts across scales. To this end, a working group within the Germany-based project, ClimXtreme, has been established to carry out multidisciplinary analyses of high-impact events. In this work, we provide a comprehensive assessment of the 29 June 2017 heavy precipitation event (HPE) affecting the Berlin metropolitan region (Germany), from the meteorological, impacts and climate perspectives, including climate change attribution. Our analysis showed that this event occurred under the influence of a mid-tropospheric trough over western Europe and two shortwave surface lows over Britain and Poland (Rasmund and Rasmund II), inducing relevant low-level wind convergence along the German–Polish border. Over 11 000 convective cells were triggered, starting early morning 29 June, displacing northwards slowly under the influence of a weak tropospheric flow (10 m s$^{−1}$ at 500 hPa). The quasi-stationary situation led to totals up to 196 mm d$^{−1}$, making this event the 29 June most severe in the 1951–2021 climatology, ranked by means of a precipitation-based index. Regarding impacts, it incurred the largest insured losses in the period 2002 to 2017 (EUR 60 million) in the greater Berlin area. We provide further insights on flood attributes (inundation, depth, duration) based on a unique household-level survey data set. The major moisture source for this event was the Alpine–Slovenian region (63 % of identified sources) due to recycling of precipitation falling over that region 1 d earlier. Implementing three different generalised extreme value (GEV) models, we quantified the return periods for this case to be above 100 years for daily aggregated precipitation, and up to 100 and 10 years for 8 and 1 h aggregations, respectively. The conditional attribution demonstrated that warming since the pre-industrial era caused a small but significant increase of 4 % in total precipitation and 10 % for extreme intensities. The possibility that not just greenhouse-gas-induced warming, but also anthropogenic aerosols affected the intensity of precipitation is investigated through aerosol sensitivity experiments. Our multi-disciplinary approach allowed us to relate interconnected aspects of extreme precipitation. For instance, the link between the unique meteorological conditions of this case and its very large return periods, or the extent to which it is attributable to already-observed anthropogenic climate change

Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area

Extreme precipitation is a weather phenomenon with tremendous damaging potential for property and human life. As the intensity and frequency of such events is projected to increase in a warming climate, there is an urgent need to advance the existing knowledge on extreme precipitation processes, statistics and impacts across scales. To this end, a working group within the Germany-based project, ClimXtreme, has been established to carry out multidisciplinary analyses of high-impact events. In this work, we provide a comprehensive assessment of the 29 June 2017 heavy precipitation event (HPE) affecting the Berlin metropolitan region (Germany), from the meteorological, impacts and climate perspectives, including climate change attribution. Our analysis showed that this event occurred under the influence of a mid-tropospheric trough over western Europe and two shortwave surface lows over Britain and Poland (Rasmund and Rasmund II), inducing relevant low-level wind convergence along the German–Polish border. Over 11 000 convective cells were triggered, starting early morning 29 June, displacing northwards slowly under the influence of a weak tropospheric flow (10 m s−1 at 500 hPa). The quasi-stationary situation led to totals up to 196 mm d−1, making this event the 29 June most severe in the 1951–2021 climatology, ranked by means of a precipitation-based index. Regarding impacts, it incurred the largest insured losses in the period 2002 to 2017 (EUR 60 million) in the greater Berlin area. We provide further insights on flood attributes (inundation, depth, duration) based on a unique household-level survey data set. The major moisture source for this event was the Alpine–Slovenian region (63 % of identified sources) due to recycling of precipitation falling over that region 1 d earlier. Implementing three different generalised extreme value (GEV) models, we quantified the return periods for this case to be above 100 years for daily aggregated precipitation, and up to 100 and 10 years for 8 and 1 h aggregations, respectively. The conditional attribution demonstrated that warming since the pre-industrial era caused a small but significant increase of 4 % in total precipitation and 10 % for extreme intensities. The possibility that not just greenhouse-gas-induced warming, but also anthropogenic aerosols affected the intensity of precipitation is investigated through aerosol sensitivity experiments. Our multi-disciplinary approach allowed us to relate interconnected aspects of extreme precipitation. For instance, the link between the unique meteorological conditions of this case and its very large return periods, or the extent to which it is attributable to already-observed anthropogenic climate change

How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.</p