Articles | Volume 24, issue 6
https://doi.org/10.5194/hess-24-3157-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-3157-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden
Marc Schleiss
CORRESPONDING AUTHOR
Dept. of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands
Jonas Olsson
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute SMHI, Norrkoping, Sweden
Peter Berg
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute SMHI, Norrkoping, Sweden
Tero Niemi
Dept. of Built Environment, Aalto University, Espoo, Finland
Finnish Meteorological Institute FMI, Helsinki, Finland
Teemu Kokkonen
Dept. of Built Environment, Aalto University, Espoo, Finland
Søren Thorndahl
Dept. of Civil Engineering, Aalborg University, Aalborg, Denmark
Rasmus Nielsen
Dept. of Civil Engineering, Aalborg University, Aalborg, Denmark
Jesper Ellerbæk Nielsen
Dept. of Civil Engineering, Aalborg University, Aalborg, Denmark
Denica Bozhinova
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute SMHI, Norrkoping, Sweden
Seppo Pulkkinen
Finnish Meteorological Institute FMI, Helsinki, Finland
Faculty of Electrical and Computer Engineering, Colorado State University, Fort Collins, USA
Related authors
Martin Fencl and Marc Schleiss
Atmos. Meas. Tech., 18, 4467–4482, https://doi.org/10.5194/amt-18-4467-2025, https://doi.org/10.5194/amt-18-4467-2025, 2025
Short summary
Short summary
A novel disaggregation algorithm for commercial microwave links (CMLs), named CLEAR (CML Segments with Equal Amounts of Rain), is proposed. CLEAR utilizes a multiplicative random cascade generator to control the splitting of link segments. The evaluation performed both on virtual and real CML data shows that CLEAR outperforms a commonly used benchmark algorithm. Moreover, the stochastic nature of CLEAR allows it to represent uncertainty as an ensemble of rain rate distributions along CML paths.
Marc Schleiss
Atmos. Meas. Tech., 17, 4789–4802, https://doi.org/10.5194/amt-17-4789-2024, https://doi.org/10.5194/amt-17-4789-2024, 2024
Short summary
Short summary
Research is conducted to identify special rainfall patterns in the Netherlands using multiple types of rainfall sensors. A total of eight potentially unique events are analyzed, considering both the number and size of raindrops. However, no clear evidence supporting the existence of a special rainfall regime could be found. The results highlight the challenges in experimentally confirming well-established theoretical ideas in the field of precipitation sciences.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 17, 235–245, https://doi.org/10.5194/amt-17-235-2024, https://doi.org/10.5194/amt-17-235-2024, 2024
Short summary
Short summary
A common method to retrieve important information about the microphysical structure of rain (DSD retrievals) requires a constrained relationship between the drop size distribution parameters. The most widely accepted empirical relationship is between μ and Λ. The relationship shows variability across the different types of rainfall (convective or stratiform). The new proposed power-law model to represent the μ–Λ relation provides a better physical interpretation of the relationship coefficients.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 15, 4951–4969, https://doi.org/10.5194/amt-15-4951-2022, https://doi.org/10.5194/amt-15-4951-2022, 2022
Short summary
Short summary
Knowledge of the raindrop size distribution (DSD) is crucial for understanding rainfall microphysics and quantifying uncertainty in quantitative precipitation estimates. In this study a general overview of the DSD retrieval approach from a polarimetric radar is discussed, highlighting sensitivity to potential sources of errors, either directly linked to the radar measurements or indirectly through the critical modeling assumptions behind the method such as the shape–size (μ–Λ) relationship.
Anna Špačková, Vojtěch Bareš, Martin Fencl, Marc Schleiss, Joël Jaffrain, Alexis Berne, and Jörg Rieckermann
Earth Syst. Sci. Data, 13, 4219–4240, https://doi.org/10.5194/essd-13-4219-2021, https://doi.org/10.5194/essd-13-4219-2021, 2021
Short summary
Short summary
An original dataset of microwave signal attenuation and rainfall variables was collected during 1-year-long field campaign. The monitored 38 GHz dual-polarized commercial microwave link with a short sampling resolution (4 s) was accompanied by five disdrometers and three rain gauges along its path. Antenna radomes were temporarily shielded for approximately half of the campaign period to investigate antenna wetting impacts.
Didier de Villiers, Marc Schleiss, Marie-Claire ten Veldhuis, Rolf Hut, and Nick van de Giesen
Atmos. Meas. Tech., 14, 5607–5623, https://doi.org/10.5194/amt-14-5607-2021, https://doi.org/10.5194/amt-14-5607-2021, 2021
Short summary
Short summary
Ground-based rainfall observations across the African continent are sparse. We present a new and inexpensive rainfall measuring instrument (the intervalometer) and use it to derive reasonably accurate rainfall rates. These are dependent on a fundamental assumption that is widely used in parameterisations of the rain drop size distribution. This assumption is tested and found to not apply for most raindrops but is still useful in deriving rainfall rates. The intervalometer shows good potential.
Jenna Ritvanen, Martin Aregger, Dmitri Moisseev, Urs Germann, Alessandro Hering, and Seppo Pulkkinen
EGUsphere, https://doi.org/10.5194/egusphere-2025-5697, https://doi.org/10.5194/egusphere-2025-5697, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Convective storms pose several hazards, like heavy rainfall, but operational short-term forecasting (nowcasting) suffers from limited models of storm development. Cell tracking, commonly used for nowcasting of convective storms and analyzing storm development, is complicated by splits and merges. We show how splits and merges can be integrated into cell track analysis, using case studies and analysis of split and merge events with operational data from the Swiss weather radar network.
Martin Fencl and Marc Schleiss
Atmos. Meas. Tech., 18, 4467–4482, https://doi.org/10.5194/amt-18-4467-2025, https://doi.org/10.5194/amt-18-4467-2025, 2025
Short summary
Short summary
A novel disaggregation algorithm for commercial microwave links (CMLs), named CLEAR (CML Segments with Equal Amounts of Rain), is proposed. CLEAR utilizes a multiplicative random cascade generator to control the splitting of link segments. The evaluation performed both on virtual and real CML data shows that CLEAR outperforms a commonly used benchmark algorithm. Moreover, the stochastic nature of CLEAR allows it to represent uncertainty as an ensemble of rain rate distributions along CML paths.
Louise Petersson Wårdh, Hasan Hosseini, Remco van de Beek, Jafet C. M. Andersson, Hossein Hashemi, and Jonas Olsson
EGUsphere, https://doi.org/10.5194/egusphere-2025-2820, https://doi.org/10.5194/egusphere-2025-2820, 2025
Short summary
Short summary
Extreme rainfall can cause severe damage, especially in cities. However, national meteorological institutes have difficulties to observe such events. In this study we show that rainfall observations collected by local actors, such as municipalities and even citizens, can contribute to better rainfall observations. Sweden’s official monitoring network could not capture the event under study, whereas the complementary sensors contributed to a better understanding of the magnitude of the event.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary
Short summary
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Marc Schleiss
Atmos. Meas. Tech., 17, 4789–4802, https://doi.org/10.5194/amt-17-4789-2024, https://doi.org/10.5194/amt-17-4789-2024, 2024
Short summary
Short summary
Research is conducted to identify special rainfall patterns in the Netherlands using multiple types of rainfall sensors. A total of eight potentially unique events are analyzed, considering both the number and size of raindrops. However, no clear evidence supporting the existence of a special rainfall regime could be found. The results highlight the challenges in experimentally confirming well-established theoretical ideas in the field of precipitation sciences.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
Short summary
Short summary
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Hideo Amaguchi, Jonas Olsson, Akira Kawamura, and Yoshiyuki Imamura
Proc. IAHS, 386, 133–140, https://doi.org/10.5194/piahs-386-133-2024, https://doi.org/10.5194/piahs-386-133-2024, 2024
Short summary
Short summary
In this research, event-based simulations were conducted using inputs from a regional climate model, providing a resolution of 5 km and updating every 10 min for both present and future climate scenarios. The findings suggest that future storms may lead to increased flooding in the watershed. This study highlights the importance of using high-resolution data to understand and prepare for the potential impacts of climate change on urban rivers.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 17, 235–245, https://doi.org/10.5194/amt-17-235-2024, https://doi.org/10.5194/amt-17-235-2024, 2024
Short summary
Short summary
A common method to retrieve important information about the microphysical structure of rain (DSD retrievals) requires a constrained relationship between the drop size distribution parameters. The most widely accepted empirical relationship is between μ and Λ. The relationship shows variability across the different types of rainfall (convective or stratiform). The new proposed power-law model to represent the μ–Λ relation provides a better physical interpretation of the relationship coefficients.
Jafet C. M. Andersson, Jonas Olsson, Remco (C. Z.) van de Beek, and Jonas Hansryd
Earth Syst. Sci. Data, 14, 5411–5426, https://doi.org/10.5194/essd-14-5411-2022, https://doi.org/10.5194/essd-14-5411-2022, 2022
Short summary
Short summary
This article presents data from three types of sensors for rain measurement, i.e. commercial microwave links (CMLs), gauges, and weather radar. Access to CML data is typically restricted, which limits research and applications. We openly share a large CML database (364 CMLs at 10 s resolution with true coordinates), along with 11 gauges and one radar composite. This opens up new opportunities to study CMLs, to benchmark algorithms, and to investigate how multiple sensors can best be combined.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 15, 4951–4969, https://doi.org/10.5194/amt-15-4951-2022, https://doi.org/10.5194/amt-15-4951-2022, 2022
Short summary
Short summary
Knowledge of the raindrop size distribution (DSD) is crucial for understanding rainfall microphysics and quantifying uncertainty in quantitative precipitation estimates. In this study a general overview of the DSD retrieval approach from a polarimetric radar is discussed, highlighting sensitivity to potential sources of errors, either directly linked to the radar measurements or indirectly through the critical modeling assumptions behind the method such as the shape–size (μ–Λ) relationship.
Peter Berg, Thomas Bosshard, Wei Yang, and Klaus Zimmermann
Geosci. Model Dev., 15, 6165–6180, https://doi.org/10.5194/gmd-15-6165-2022, https://doi.org/10.5194/gmd-15-6165-2022, 2022
Short summary
Short summary
When performing impact analyses with climate models, one is often confronted with the issue that the models have significant bias. Commonly, the modelled climatological temperature deviates from the observed climate by a few degrees or it rains excessively in the model. MIdAS employs a novel statistical model to translate the model climatology toward that observed using novel methodologies and modern tools. The coding platform allows opportunities to develop methods for high-resolution models.
Erika Médus, Emma D. Thomassen, Danijel Belušić, Petter Lind, Peter Berg, Jens H. Christensen, Ole B. Christensen, Andreas Dobler, Erik Kjellström, Jonas Olsson, and Wei Yang
Nat. Hazards Earth Syst. Sci., 22, 693–711, https://doi.org/10.5194/nhess-22-693-2022, https://doi.org/10.5194/nhess-22-693-2022, 2022
Short summary
Short summary
We evaluate the skill of a regional climate model, HARMONIE-Climate, to capture the present-day characteristics of heavy precipitation in the Nordic region and investigate the added value provided by a convection-permitting model version. The higher model resolution improves the representation of hourly heavy- and extreme-precipitation events and their diurnal cycle. The results indicate the benefits of convection-permitting models for constructing climate change projections over the region.
Anna Špačková, Vojtěch Bareš, Martin Fencl, Marc Schleiss, Joël Jaffrain, Alexis Berne, and Jörg Rieckermann
Earth Syst. Sci. Data, 13, 4219–4240, https://doi.org/10.5194/essd-13-4219-2021, https://doi.org/10.5194/essd-13-4219-2021, 2021
Short summary
Short summary
An original dataset of microwave signal attenuation and rainfall variables was collected during 1-year-long field campaign. The monitored 38 GHz dual-polarized commercial microwave link with a short sampling resolution (4 s) was accompanied by five disdrometers and three rain gauges along its path. Antenna radomes were temporarily shielded for approximately half of the campaign period to investigate antenna wetting impacts.
Didier de Villiers, Marc Schleiss, Marie-Claire ten Veldhuis, Rolf Hut, and Nick van de Giesen
Atmos. Meas. Tech., 14, 5607–5623, https://doi.org/10.5194/amt-14-5607-2021, https://doi.org/10.5194/amt-14-5607-2021, 2021
Short summary
Short summary
Ground-based rainfall observations across the African continent are sparse. We present a new and inexpensive rainfall measuring instrument (the intervalometer) and use it to derive reasonably accurate rainfall rates. These are dependent on a fundamental assumption that is widely used in parameterisations of the rain drop size distribution. This assumption is tested and found to not apply for most raindrops but is still useful in deriving rainfall rates. The intervalometer shows good potential.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
Short summary
Short summary
This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Jonas Olsson, Peter Berg, and Remco van de Beek
Adv. Sci. Res., 18, 59–64, https://doi.org/10.5194/asr-18-59-2021, https://doi.org/10.5194/asr-18-59-2021, 2021
Short summary
Short summary
We have developed a tool to visualize rainfall observations, based on a combination of meteorological stations and weather radars, over Sweden in near real-time. By accumulating the rainfall in time (1–12 h) and space (hydrological basins), the tool is designed mainly for hydrological applications, e.g. to support flood forecasters and to facilitate post-event analyses. Despite evident uncertainties, different users have confirmed an added value of the tool in case studies.
Peter Berg, Fredrik Almén, and Denica Bozhinova
Earth Syst. Sci. Data, 13, 1531–1545, https://doi.org/10.5194/essd-13-1531-2021, https://doi.org/10.5194/essd-13-1531-2021, 2021
Short summary
Short summary
HydroGFD3.0 (Hydrological Global Forcing Data) is a data set of daily precipitation and temperature intended for use in hydrological modelling. The method uses different observational data sources to correct the variables from a model estimation of precipitation and temperature. An openly available data set covers the years 1979–2019, and times after this are available by request.
Torben Schmith, Peter Thejll, Peter Berg, Fredrik Boberg, Ole Bøssing Christensen, Bo Christiansen, Jens Hesselbjerg Christensen, Marianne Sloth Madsen, and Christian Steger
Hydrol. Earth Syst. Sci., 25, 273–290, https://doi.org/10.5194/hess-25-273-2021, https://doi.org/10.5194/hess-25-273-2021, 2021
Short summary
Short summary
European extreme precipitation is expected to change in the future; this is based on climate model projections. But, since climate models have errors, projections are uncertain. We study this uncertainty in the projections by comparing results from an ensemble of 19 climate models. Results can be used to give improved estimates of future extreme precipitation for Europe.
Cited articles
Anagnostou, M. N., Kalogiros, J., Anagnostou, E. N., Tarolli, M., Papadopoulos,
A., and Borga, M.: Performance evaluation of high-resolution rainfall
estimation by X-band dual-polarization radar for flash flood applications in
mountainous basins, J. Hydrol., 394, 4–16,
https://doi.org/10.1016/j.jhydrol.2010.06.026, 2010. a
Andréassian, V., Perrin, C., Michel, C., Usart-Sanchez, I., and Lavabre,
J.: Impact of imperfect rainfall knowledge on the efficiency and the
parameters of watershed models, J. Hydrol., 250, 206–223,
https://doi.org/10.1016/S0022-1694(01)00437-1, 2001. a
Aronica, G., Freni, G., and Oliveri, E.: Uncertainty analysis of the influence
of rainfall time resolution in the modelling of urban drainage systems,
Hydrol. Process., 19, 1055–1071, https://doi.org/10.1002/hyp.5645, 2005. a, b
Baeck, M. L. and Smith, J. A.: Rainfall Estimation by the WSR-88D for Heavy
Rainfall Events, Weather Forecast., 13, 416–436,
https://doi.org/10.1175/1520-0434(1998)013<0416:REBTWF>2.0.CO;2, 1998. a
Bech, J., Codina, B., Lorente, J., and Bebbington, D.: The Sensitivity of
Single Polarization Weather Radar Beam Blockage Correction to Variability in
the Vertical Refractivity Gradient, J. Atmos. Ocean. Tech., 20,
845–855, https://doi.org/10.1175/1520-0426(2003)020<0845:TSOSPW>2.0.CO;2, 2003. a
Berg, P., Norin, L., and Olsson, J.: Creation of a high resolution
precipitation data set by merging gridded gauge data and radar observations
for Sweden, J. Hydrol., 541, 6–13, https://doi.org/10.1016/j.jhydrol.2015.11.031,
2016. a, b
Berne, A. and Krajewski, W. F.: Radar for hydrology: Unfulfilled promise or
unrecognized potential?, Adv. Water Resour., 51, 357–366,
https://doi.org/10.1016/j.advwatres.2012.05.005, 2013. a, b
Berne, A., Delrieu, G., Creutin, J.-D., and Obled, C.: Temporal and spatial
resolution of rainfall measurements required for urban hydrology, J. Hydrol.,
299, 166–179, https://doi.org/10.1016/j.jhydrol.2004.08.002, 2004. a, b
Blenkinsop, S., Lewis, E., Chan, S. C., and Fowler, H. J.: Quality-control of
an hourly rainfall dataset and climatology of extremes for the UK, Int. J.
Climatol., 37, 722–740, https://doi.org/10.1002/joc.4735, 2017. a
Brandes, E. A., Ryzhkov, A. V., and Zrnic, D. S.: An evaluation of radar
rainfall estimates from specific differential phase, J. Atmos. Ocean.
Tech., 18, 363–375,
https://doi.org/10.1175/1520-0426(2001)018<0363:AEORRE>2.0.CO;2, 2001. a
Bringi, V. N. and Chandrasekar, V.: Polarimetric doppler weather radar,
Cambridge University Press, Cambridge, 2001. a
Bruni, G., Reinoso, R., van de Giesen, N. C., Clemens, F. H. L. R., and ten Veldhuis, J. A. E.: On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution, Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, 2015. a, b, c
Chandrasekar, V., Keranen, R., Lim, S., and Moisseev, D.: Recent advances in
classification of observations from dual polarization weather radars, Atmos.
Res., 119, 97–111, https://doi.org/10.1016/j.atmosres.2011.08.014, 2013. a
Chang, M. and Flannery, L. A.: Spherical gauges for improving the accuracy of
rainfall measurements, Hydrol. Process., 15, 643–654,
https://doi.org/10.1002/hyp.181, 2001. a
Ciach, G. J.: Local random errors in tipping-bucket rain gauge measurements, J.
Atmos. Ocean. Tech., 20, 752–759,
https://doi.org/10.1175/1520-0426(2003)20<752:LREITB>2.0.CO;2, 2003. a
Ciach, G. J. and Krajewski, W. F.: On the estimation of radar rainfall error
variance, Adv. Water Resour., 22, 585–595,
https://doi.org/10.1016/S0309-1708(98)00043-8, 1999a. a, b, c
Ciach, G. J. and Krajewski, W. F.: Radar-Rain Gauge Comparisons under
Observational Uncertainties, J. Appl. Meteorol., 38, 1519–1525,
https://doi.org/10.1175/1520-0450(1999)038<1519:RRGCUO>2.0.CO;2, 1999b. a
Collier, C. G.: Flash flood forecasting: What are the limits of
predictability?, Q. J. Roy. Meteor. Soc., 133, 3–23, https://doi.org/10.1002/qj.29,
2007. a
Collier, C. G. and Knowles, J. M.: Accuracy of rainfall estimates by radar,
part III: application for short-term flood forecasting, J. Hydrol., 83,
237–249, https://doi.org/10.1016/0022-1694(86)90154-X, 1986. a
Courty, L. G., Rico-Ramirez, M. A., and Pedrozo-Acuna, A.: The Significance of
the Spatial Variability of Rainfall on the Numerical Simulation of Urban
Floods, Water, 10, 1–17, https://doi.org/10.3390/w10020207, 2018. a, b
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017. a
Cunha, L. K., Mandapaka, P. V., Krajewski, W. F., Mantilla, R., and Bradley,
A. A.: Impact of radar-rainfall error structure on estimated flood magnitude
across scales: An investigation based on a parsimonious distributed
hydrological model, Water Resour. Res., 48, W10515,
https://doi.org/10.1029/2012WR012138, 2012. a
Cunha, L. K., Smith, J. A., Krajewski, W. F., Baeck, M. L., and Seo, B.-C.:
NEXRAD NWS Polarimetric Precipitation Product Evaluation for IFloodS, J.
Hydrometeorol., 16, 1676–1699, https://doi.org/10.1175/JHM-D-14-0148.1, 2015. a, b
Dai, Q. and Han, D.: Exploration of discrepancy between radar and gauge
rainfall estimates driven by wind fields, Water Resour. Res., 50, 8571–8588,
https://doi.org/10.1002/2014WR015794, 2014. a, b
Delrieu, G., Nicol, J., Yates, E., Kirstetter, P.-E., Creutin, J.-D., Anquetin,
S., Obled, C., Saulnier, G.-M., Ducrocq, V., Gaume, E., Payrastre, O.,
Andrieu, H., Ayral, P.-A., Bouvier, C., Neppel, L., Livet, M., Lang, M.,
du Châtelet, J., Walpersdorf, A., and Wobrock, W.: The Catastrophic
Flash-Flood Event of 8-9 September 2002 in the Gard Region, France: A First
Case Study for the Cévennes-Vivarais Mediterranean Hydrometeorological
Observatory, J. Hydrometeorol., 6, 34–52, https://doi.org/10.1175/JHM-400.1, 2005. a
Delrieu, G., Wijbrans, A., Boudevillain, B., Faure, D., Bonnifait, L., and
Kirstetter, P.-E.: Geostatistical radar-raingauge merging: A novel method
for the quantification of rain estimation accuracy, Adv. Water Resour., 71,
110–124, https://doi.org/10.1016/j.advwatres.2014.06.005, 2014. a, b
Dupasquier, B., Andrieu, H., Delrieu, G., Griffith, R. J., and Cluckie, I.:
Influence of the VRP on High Frequency Fluctuations Between Radar and
Raingage Data, Phys. Chem. Earth, 25, 1021–1025,
https://doi.org/10.1016/S1464-1909(00)00146-5, 2000. a
Einfalt, T., Arnbjerg-Nielsen, K., Golz, C., Jensen, N. E., Quirmbach, M.,
Vaes, G., and Vieux, B.: Towards a roadmap for use of radar rainfall data in
urban drainage, J. Hydrol., 299, 186–202,
https://doi.org/10.1016/j.jhydrol.2004.08.004, 2004. a
Fairman, J. G., Schultz, D. M., Kirshbaum, D. J., Gray, S. L., and Barrett,
A. I.: Climatology of Size, Shape, and Intensity of Precipitation Features
over Great Britain and Ireland, J. Hydrometeorol., 18, 1595–1615,
https://doi.org/10.1175/JHM-D-16-0222.1, 2017. a
Goudenhoofdt, E. and Delobbe, L.: Evaluation of radar-gauge merging methods for quantitative precipitation estimates, Hydrol. Earth Syst. Sci., 13, 195–203, https://doi.org/10.5194/hess-13-195-2009, 2009. a
Goudenhoofdt, E., Delobbe, L., and Willems, P.: Regional frequency analysis of extreme rainfall in Belgium based on radar estimates, Hydrol. Earth Syst. Sci., 21, 5385–5399, https://doi.org/10.5194/hess-21-5385-2017, 2017. a, b, c
Gourley, J. J., Tabary, P., and Parent-du Chatelet, J.: Data quality of the
Meteo-France C-band polarimetric radar, J. Atmos. Ocean. Tech., 23,
1340–1356, https://doi.org/10.1175/JTECH1912.1, 2006. a
Gourley, J. J., Tabary, P., and Parent-du Chatelet, J.: A fuzzy logic algorithm
for the separation of precipitating from nonprecipitating echoes using
polarimetric radar observations, J. Atmos. Ocean. Tech., 24, 1439–1451,
https://doi.org/10.1175/JTECH2035.1, 2007. a
Gu, J.-Y., Ryzhkov, A., Zhang, P., Neilley, P., Knight, M., Wolf, B., and Lee,
D.-I.: Polarimetric Attenuation Correction in Heavy Rain at C Band, J.
Appl. Meteorol. Clim., 50, 39–58, https://doi.org/10.1175/2010JAMC2258.1, 2011. a
He, X., Sonnenborg, T. O., Refsgaard, J. C., Vejen, F., and Jensen, K. H.:
Evaluation of the value of radar QPE data and rain gauge data for
hydrological modeling, Water Resour. Res., 49, 5989–6005,
https://doi.org/10.1002/wrcr.20471, 2013. a, b
Holleman, I.: Bias adjustment and long-term verification of radar-based
precipitation estimates, Meteorol. Appl., 14, 195–203, https://doi.org/10.1002/met.22,
2007. a, b
Holleman, I. and Beekhuis, H.: Review of the KNMI clutter removal scheme, Tech.
Rep. TR-284, Royal Netherlands Meteorological Institute KNMI, available
at: https://www.knmi.nl/publications/fulltexts (last access: 15 June 2020), 2005. a
Holleman, I., Huuskonen, A., Kurri, M., and Beekhuis, H.: Operational
monitoring of weather radar receiving chain using the sun, J. Atmos. Ocean. Tech., 27, 159–166, https://doi.org/10.1175/2009JTECHA1213.1, 2010. a
Huuskonen, A., Saltikoff, E., and Holleman, I.: The Operational Weather Radar
Network in Europe, B. Am. Meteorol. Soc., 95, 897–907,
https://doi.org/10.1175/BAMS-D-12-00216.1, 2014. a
KNMI: Handbook for the Meteorological Observation, Tech. rep., Koninklijk
Nederlands Meteorologisch Instituut, De Bilt, Netherlands, available at: http://projects.knmi.nl/hawa/pdf/Handbook_H01_H06.pdf (last access: 15 June 2020), 2000. a
Koistinen, J. and Pohjola, H.: Estimation of Ground-Level Reflectivity Factor
in Operational Weather Radar Networks Using VPR-Based Correction Ensembles,
J. Appl. Meteorol. Clim., 53, 2394–2411, https://doi.org/10.1175/JAMC-D-13-0343.1, 2014. a
Krajewski, W. F.: Cokriging radar-rainfall and rain-gauge data, J. Geophys.
Res.-Atmos., 90, 9571–9580, https://doi.org/10.1029/JD092iD08p09571, 1987. a
Krajewski, W. F. and Smith, J. A.: Radar hydrology: rainfall estimation, Adv.
Water Resour., 25, 1387–1394, https://doi.org/10.1016/j.advwatres.2005.03.018, 2002. a
Krajewski, W. F., Villarini, G., and Smith, J. A.: RADAR-Rainfall
Uncertainties: Where are we after Thirty Years of Effort?, B. Am.
Meteor. Soc., 91, 87–94, https://doi.org/10.1175/2009BAMS2747.1, 2010. a, b, c
Lee, G.: Sources of errors in rainfall measurements by polarimetric radar:
variability of drop size distributions, observational noise, and variation of
relationships between R and polarimetric parameters, J. Atmos. Ocean. Tech., 23, 1005–1028, 2006. a
Leinonen, J., Moisseev, D., Leskinen, M., and Petersen, W. A.: A Climatology
of Disdrometer Measurements of Rainfall in Finland over Five Years with
Implications for Global Radar Observations, J. Appl. Meteorol. Clim., 51,
392–404, https://doi.org/10.1175/JAMC-D-11-056.1, 2012. a, b
Löwe, R., Thorndahl, S., Mikkelsen, P. S., Rasmussen, M. R., and Madsen,
H.: Probabilistic online runoff forecasting for urban catchments using inputs
from rain gauges as well as statically and dynamically adjusted weather
radar, J. Hydrol., 512, 397–407, https://doi.org/10.1016/j.jhydrol.2014.03.027, 2014. a, b
Madsen, H., Mikkelsen, P. S., Rosbjerg, D., and Harremoës, P.: Estimation
of regional intensity-duration-frequency curves for extreme precipitation,
Water Sci. Technol., 37, 29–36, https://doi.org/10.1016/S0273-1223(98)00313-8, 1998. a, b
Madsen, H., Gregersen, I. B., Rosbjerg, D., and Arnbjerg-Nielsen, K.: Regional
frequency analysis of short duration rainfall extremes using gridded daily
rainfall data as co-variate, Water Sci. Technol., 75, 1971–1981,
https://doi.org/10.2166/wst.2017.089, 2017. a, b
Matrosov, S. Y., Cifelli, R., Kennedy, P. C., Nesbitt, S. W., Rutledge, S. A.,
Bringi, V. N., and Martner, B. E.: A comparative study of rainfall
retrievals based on specific differential phase shifts at X- and S-band radar
frequencies, J. Atmos. Ocean. Tech., 23, 952–963,
https://doi.org/10.1175/JTECH1887.1, 2006. a
Matrosov, S. Y., Clark, K. A., and Kingsmill, D. E.: A polarimetric radar
approach to identify rain, melting-layer, and snow regions for applying
corrections to vertical profiles of reflectivity, J. Appl. Meteorol. Clim., 46,
154–166, 2007. a
Michelson, D.: The Swedish weather radar production chain, in: Proceedings
of Fourth European Conference on Radar in Meteorology and Hydrology (ERAD),
Barcelona, Spain, 382–385, 2006. a
Michelson, D., Henja, A., Ernes, S., Haase, G., Koistinen, J., Ośródka,
K., Peltonen, T., Szewczykowski, M., and Szturc, J.: BALTRAD Advanced
Weather Radar Networking, J. Open Res. Softw., 6, 1–12,
https://doi.org/10.5334/jors.193, 2018. a, b
Nielsen, J. E., Thorndahl, S. L., and Rasmussen, M. R.: A Numerical Method to
Generate High Temporal Resolution Precipitation Time Series by Combining
Weather Radar Measurements with a Nowcast Model, Atmos. Res., 138, 1–12,
https://doi.org/10.1016/j.atmosres.2013.10.015, 2014. a
Niemi, T. J., Warsta, L., Taka, M., Hickman, B., Pulkkinen, S., Krebs, G.,
Moisseev, D. N., Koivusalo, H., and Kokkonen, T.: Applicability of open
rainfall data to event-scale urban rainfall-runoff modelling, J. Hydrol.,
547, 143–155, https://doi.org/10.1016/j.jhydrol.2017.01.056, 2017. a
Norin, L., Devasthale, A., L'Ecuyer, T. S., Wood, N. B., and Smalley, M.: Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden, Atmos. Meas. Tech., 8, 5009–5021, https://doi.org/10.5194/amt-8-5009-2015, 2015. a, b
Ntelekos, A. A., Smith, J. A., and Krajewski, W. F.: Climatological Analyses
of Thunderstorms and Flash Floods in the Baltimore Metropolitan Region, J.
Hydrometeorol., 8, 88–101, https://doi.org/10.1175/JHM558.1, 2007. a
Nystuen, J. A.: Relative performance of automatic rain gauges under different
rainfall conditions, J. Atmos. Ocean. Tech., 16, 1025–1043,
https://doi.org/10.1175/1520-0426(1999)016<1025:RPOARG>2.0.CO;2, 1999. a, b
Ochoa-Rodriguez, S., Wang, L.-P., Gires, A., Pina, R. D., Reinoso-Rondinel, R.,
Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., van Assel, J., Kroll, S.,
Damian Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia,
I., Onof, C., Willems, P., and ten Veldhuis, M.-C.: Impact of spatial and
temporal resolution of rainfall inputs on urban hydrodynamic modelling
outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407,
https://doi.org/10.1016/j.jhydrol.2015.05.035, 2015. a
Ogden, F. L. and Julien, P. Y.: Runoff model sensitivity to radar rainfall
resolution, J. Hydrol., 158, 1–18, 1994. a
Otto, T. and Russchenberg, H. W. J.: Estimation of specific differential phase
and differential backscatter phase from polarimetric weather radar
measurements of rain, IEEE Geosci. Remote Sens. Lett., 8, 988–992,
https://doi.org/10.1109/LGRS.2011.2145354, 2011. a
Overeem, A., Buishand, T. A., and Holleman, I.: Extreme rainfall analysis and
estimation of depth-duration-frequency curves using weather radar, Water
Resour. Res., 45, W10424, https://doi.org/10.1029/2009WR007869, 2009a. a, b
Overeem, A., Buishand, T. A., Holleman, I., and Uijlenhoet, R.: Extreme value
modeling of areal rainfall from weather radar, Water Resour. Res., 46,
W09514, https://doi.org/10.1029/2009WR008517, 2010. a
Peleg, N., Marra, F., Fatichi, S., Paschalis, A., Molnar, P., and Burlando, P.:
Spatial variability of extreme rainfall at radar subpixel scale, J. Hydrol.,
556, 922–933, https://doi.org/10.1016/j.jhydrol.2016.05.033, 2018. a
Pollock, M. D., O'Donnell, G., Quinn, P., Dutton, M., Black, A., Wilkinson, M.,
Colli, M., Stagnaro, M., Lanza, L. G., Lewis, E., Kilsby, C. G., and
O'Connell, P. E.: Quantifying and Mitigating Wind-Induced Undercatch in
Rainfall Measurements, Water Resour. Res., 54, 3863–3875,
https://doi.org/10.1029/2017WR022421, 2018. a, b
Rafieeinasab, A., Norouzi, A., Kim, S., Habibi, H., Nazari, B., Seo, D.-J.,
Lee, H., Cosgrove, B., and Cui, Z.: Toward high-resolution flash flood
prediction in large urban areas – Analysis of sensitivity to spatiotemporal
resolution of rainfall input and hydrologic modeling, J. Hydrol., 531,
370–388, https://doi.org/10.1016/j.jhydrol.2015.08.045, 2015. a, b
Rickenbach, T. M., Nieto-Ferreira, R., Zarzar, C., and Nelson, B.: A seasonal
and diurnal climatology of precipitation organization in the southeastern
United States, Q. J. Roy. Meteor. Soc., 141, 1938–1956,
https://doi.org/10.1002/qj.2500, 2015. a
Rico-Ramirez, M. A., Liguori, S., and Schellart, A. N. A.: Quantifying
radar-rainfall uncertainties in urban drainage flow modelling, J. Hydrol.,
528, 17–28, https://doi.org/10.1016/j.jhydrol.2015.05.057, 2015. a
Rodríguez-Iturbe, I. and Mejía, J. M.: On the transformation of point
rainfall to areal rainfall, Water Resour. Res., 10, 729–735,
https://doi.org/10.1029/WR010i004p00729, 1974. a
Rossa, A., Liechti, K., Zappa, M., Bruen, M., Germann, U., Haase, G., Keil, C.,
and Krahe, P.: The COST 731 Action: a review on uncertainty propagation in
advanced hydro-meteorological forecast systems, Atmos. Res., 100, 150–167,
https://doi.org/10.1016/j.atmosres.2010.11.016, 2011. a
Ruzanski, E., Chandrasekar, V., and Wang, Y. T.: The CASA nowcasting system,
J. Atmos. Ocean. Tech., 28, 640–655, https://doi.org/10.1175/2011JTECHA1496.1,
2011. a
Ryzhkov, A. and Zrnic, D. S.: Assessment of rainfall measurement that uses
specific differential phase, J. Appl. Meteorol., 35, 2080–2090,
https://doi.org/10.1175/1520-0450(1996)035<2080:AORMTU>2.0.CO;2, 1996. a
Ryzhkov, A. V. and Zrnic, D. S.: Discrimination between rain and snow with a
polarimetric radar, J. Appl. Meteorol., 37, 1228–1240, 1998. a
Saltikoff, E., Haase, G., Delobbe, L., Gaussiat, N., Martet, M., Idziorek, D.,
Leijnse, H., Novák, P., Lukach, M., and Stephan, K.: OPERA the Radar
Project, Atmosphere, 10, 1–13, 2019. a
Schilling, W.: Rainfall data for urban hydrology: what do we need?, Atmos.
Res., 27, 5–21, https://doi.org/10.1016/0169-8095(91)90003-F, 1991. a, b
Seo, B.-C., Dolan, B., Krajewski, W. F., Rutledge, S. A., and Petersen, W.:
Comparison of Single- and Dual-Polarization-Based Rainfall Estimates Using
NEXRAD Data for the NASA Iowa Flood Studies Project, J. Hydrometeorol., 16,
1658–1675, https://doi.org/10.1175/JHM-D-14-0169.1, 2015. a, b
Sieck, L. C., Burges, S. J., and Steiner, M.: Challenges in obtaining reliable
measurements of point rainfall, Water Resour. Res., 43, W01420,
https://doi.org/10.1029/2005WR004519, 2007. a, b
Smith, J. A. and Krajewski, W. F.: Estimation of the Mean Field Bias of Radar
Rainfall Estimates, J. Appl. Meteorol., 30, 397–412,
https://doi.org/10.1175/1520-0450(1991)030<0397:EOTMFB>2.0.CO;2, 1991. a, b
Smith, J. A., Seo, D. J., Baeck, M. L., and Hudlow, M. D.: An intercomparison
study of NEXRAD precipitation estimates, Water Resour. Res., 32, 2035–2045,
https://doi.org/10.1029/96WR00270, 1996. a
Smith, J. A., Baeck, M. L., Meierdiercks, K. L., Miller, A. J., and Krajewski,
W. F.: Radar rainfall estimation for flash flood forecasting in small urban
watersheds, Adv. Water Resour., 30, 2087–2097,
https://doi.org/10.1016/j.advwatres.2006.09.007, 2007. a
Smith, J. A., Baeck, M. L., Villarini, G., Welty, C., Miller, A. J., and
Krajewski, W. F.: Analyses of a long-term, high-resolution radar rainfall
data set for the Baltimore metropolitan region, Water Resour. Res., 48,
W04504, https://doi.org/10.1029/2011WR010641, 2012. a, b
Stevenson, S. N. and Schumacher, R. S.: A 10-Year Survey of Extreme Rainfall
Events in the Central and Eastern United States Using Gridded Multisensor
Precipitation Analyses, Mon. Weather Rev., 142, 3147–3162,
https://doi.org/10.1175/MWR-D-13-00345.1, 2014. a
Stransky, D., Bares, V., and Fatka, P.: The effect of rainfall measurement
uncertainties on rainfall-runoff processes modelling, Water Sci. Technol.,
55, 103–111, 2007. a
Thomsen, R. S. T.: Drift af Spildevandskomitéens
RegnmålersystemÅrsnotat 2015, Tech. rep., DMI, Copenhagen, available at: https://www.dmi.dk/fileadmin/user_upload/Rapporter/TR/2016/DMI_Report_16_3.pdf (last
access: 13 December 2019), 2016. a
Thorndahl, S., Nielsen, J. E., and Rasmussen, M. R.: Bias adjustment and
advection interpolation of long-term high resolution radar rainfall series,
J. Hydrol., 508, 214–226, https://doi.org/10.1016/j.jhydrol.2013.10.056,
2014a. a, b
Thorndahl, S., Smith, J. A., Baeck, M. L., and Krajewski, W. F.: Analyses of
the temporal and spatial structures of heavy rainfall from a catalog of
high-resolution radar rainfall fields, Atmos. Res., 144, 111–125,
https://doi.org/10.1016/j.atmosres.2014.03.013, 2014b. a
Thorndahl, S., Nielsen, J. E., and Jensen, D. G.: Urban pluvial flood
prediction: a case study evaluating radar rainfall nowcasts and numerical
weather prediction models as model inputs, Water Sci. Technol., 74,
2599–2610, https://doi.org/10.2166/wst.2016.474, 2016. a
Thorndahl, S., Einfalt, T., Willems, P., Nielsen, J. E., ten Veldhuis, M.-C., Arnbjerg-Nielsen, K., Rasmussen, M. R., and Molnar, P.: Weather radar rainfall data in urban hydrology, Hydrol. Earth Syst. Sci., 21, 1359–1380, https://doi.org/10.5194/hess-21-1359-2017, 2017. a
Thorndahl, S. L., Nielsen, J. E., and Rasmussen, M. R.: Estimation of Storm-Centred Areal Reduction Factors from Radar Rainfall for Design in Urban Hydrology, Water, 11, 1120, https://doi.org/10.3390/w11061120, 2019. a, b
Tian, Y., Huffman, G. J., Adler, R. F., Tang, L., Sapiano, M., Maggioni, V.,
and Wu, H.: Modeling errors in daily precipitation measurements: Additive or
multiplicative?, Geophys. Res. Lett., 40, 2060–2065,
https://doi.org/10.1002/grl.50320, 2013. a
Vasiloff, S. V., Howard, K. W., and Zhang, J.: Difficulties with correcting
radar rainfall estimates based on rain gauge data: a case study of severe
weather in Montana on 16–17 June 2007, Weather Forecast., 24, 1334–1344,
https://doi.org/10.1175/2009WAF2222154.1, 2009. a, b
Vejen, F.: Teknisk rapport 06-15, Nyt SVK system, Sammenligning af
nedbørmålinger med nye og nuværende system, Tech. rep., DMI,
Copenhagen, available at: https://www.dmi.dk/fileadmin/Rapporter/TR/tr06-15.pdf (last access: 13 December 2019), 2006. a
Villarini, G., Smith, J. A., Baeck, M. L., Sturdevant-Rees, P., and Krajewski,
W. F.: Radar analyses of extreme rainfall and flooding in urban drainage
basins, J. Hydrol., 381, 266–286, https://doi.org/10.1016/j.jhydrol.2009.11.048, 2010. a
Wang, Y. and Chandrasekar, V.: Algorithm for Estimation of the Specific
Differential Phase, J. Atmos. Ocean. Tech., 26, 2565–2578,
https://doi.org/10.1175/2009JTECHA1358.1, 2009. a
Wang, Y. T. and Chandrasekar, V.: Quantitative precipitation estimation in the
CASA X-band dual-polarization radar network, J. Atmos. Ocean. Tech., 27,
1665–1676, https://doi.org/10.1175/2010JTECHA1419.1, 2010. a, b
Wessels, H. R. A. and Beekhuis, J. H.: Stepwise procedure for suppression of
anomalous ground clutter, in: Proc. COST-75, Weather Radar
Systems, International Seminar, Brussels, Belgium, 270–277, 1995. a
WMO: Guide to Meteorological Instruments and Methods of Observation,
WMO-No.8, World Meteorological Organization, Geneva, 7th ed. edn., 2008. a
Wójcik, O. P., Holt, J., Kjerulf, A., Müller, L., Ethelberg, S., and
Molbak, K.: Personal protective equipment, hygiene behaviours and
occupational risk of illness after July 2011 flood in Copenhagen, Denmark,
Epidemiol. Infect., 141, 1756–1763, https://doi.org/10.1017/S0950268812002038,
2013. a
Wood, S. J., Jones, D. A., and Moore, R. J.: Accuracy of rainfall measurement for scales of hydrological interest, Hydrol. Earth Syst. Sci., 4, 531–543, https://doi.org/10.5194/hess-4-531-2000, 2000. a
Wright, D. B., Smith, J. A., Villarini, G., and Baeck, M. L.: Hydroclimatology
of flash flooding in Atlanta, Water Resour. Res., 48, W04524,
https://doi.org/10.1029/2011WR011371, 2012. a
Wright, D. B., Smith, J. A., Villarini, G., and Baeck, M. L.: Long-Term
High-Resolution Radar Rainfall Fields for Urban Hydrology, J. Am. Water
Resour. As., 50, 713–734, https://doi.org/10.1111/jawr.12139, 2014. a, b
Yang, L., Smith, J., Baeck, M. L., Smith, B., Tian, F., and Niyogi, D.:
Structure and evolution of flash flood producing storms in a small urban
watershed, J. Geophys. Res.-Atmos., 121, 3139–3152,
https://doi.org/10.1002/2015JD024478, 2016.
a
Yoo, C., Park, C., Yoon, J., and Kim, J.: Interpretation of mean-field bias
correction of radar rain rate using the concept of linear regression, Hydrol.
Process., 28, 5081–5092, https://doi.org/10.1002/hyp.9972, 2014. a
Young, C. B., Bradley, A. A., Krajewski, W. F., Kruger, A., and Morrisey,
M. L.: Evaluating NEXRAD multisensor precipitation estimates for operational
hydrologic forecasting, J. Hydrometeorol., 1, 241–254, 2000. a
Zhou, Z., Smith, J. A., Yang, L., Baeck, M. L., Chaney, M., Ten Veldhuis,
M.-C., Deng, H., and Liu, S.: The complexities of urban flood response:
Flood frequency analyses for the Charlotte metropolitan region, Water
Resour. Res., 53, 7401–7425, https://doi.org/10.1002/2016WR019997, 2017. a
Zrnic, D. S. and Ryzhkov, A. V.: Advantages of rain measurements using specific
differential phase, J. Atmos. Ocean. Tech., 13, 454–464,
https://doi.org/10.1175/1520-0426(1996)013<0454:AORMUS>2.0.CO;2, 1996. a, b
Zrnic, D. S. and Ryzhkov, A. V.: Polarimetry for weather surveillance radars,
B. Am. Meteor. Soc., 80, 389–406, 1999. a
Short summary
A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
A multinational assessment of radar's ability to capture heavy rain events is conducted. In...