Abstract

Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

Highlights

  • The ability to measure short-duration, high-intensity rainfall rates is of paramount importance in predicting hydrological response

  • Based on the values of the G/R ratio in Fig. 5, the Dutch C-band radar composite has the lowest apparent bias of all products (28.4 %), followed by Finland (35.9 %), Denmark (37.3 %) and Sweden (39.7 %). Such direct comparisons are not really fair, as they do not take into account the different spatial and temporal resolutions of the radar products, the number of radars used during the estimation and their distances to the considered rain gauges

  • It is important to keep in mind that multiplicative biases in the Danish X-band radar product were assessed on the basis of 5 min tipping bucket rain gauge

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Summary

Introduction

The ability to measure short-duration, high-intensity rainfall rates is of paramount importance in predicting hydrological response. Smith et al (2012), Wright et al (2014), Thorndahl et al (2014b) and Cunha et al (2015) highlighted several major quality issues affecting post-processed quantitative precipitation estimates from NEXRAD, including rangedependent and intensity-dependent biases Quantifying these residual errors and studying their propagation in hydrological models is crucial for improving the timing and accuracy of flood predictions (Cunha et al, 2012; Bruni et al, 2015; Courty et al, 2018; Niemi et al, 2017). By comparing different types of radar products (C-band versus X-band, single versus dual polarization) and identifying the main sources of errors and biases across scales, important recommendations about how to improve the accuracy of quantitative precipitation estimates for flash flood prediction and urban pluvial flooding can be drawn.

Event selection and data extraction methods
Radar data for Denmark
Radar data for the Netherlands
Radar data for Finland
Radar data for Sweden
Additional radar products
Comparison of radar and gauge measurements
Bias estimation
Peak intensity bias
Other metrics
Agreement during the four most intense events
Overall agreement between radar and gauges
Conditional bias with intensity
Other sources of bias
Agreement during the peaks
Results for the additional radar products
Conclusions
Full Text
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