The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

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 moving to higher resolution and making use of dual-polarization, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. But 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 hours. 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, 6 different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events for each country were used to quantify the overall agreement between radar and gauges and the errors affecting the peaks. Results show that the overall agreement between radar and gauges in heavy rain is fair, with multiplicative biases in the order of 1.41–1.66 (i.e., radar underestimates by 29–39.8 %) and correlation coefficients of 0.71–0.83 across countries. However, the bias increases with intensity, reaching 45.9 %–66.2 % during the peaks. Only part of the bias (i.e., roughly 13 %–30 % depending on the radar product) can be explained by differences in measurement areas between gauges and radar. Radar products with higher spatial and temporal resolutions agreed better with the gauges, highlighting the importance of high-resolution radar for urban hydrology. However, for capturing peak intensity and reducing the bias during the most intense part of a storm, the ability to combine measurements from multiple overlapping radars to help mitigate attenuation seemed to play a more important role than resolution. The use of dual-polarization and phase information (e.g., Kdp) in the experimental Finnish OSAPOL product also seemed to provide a slight advantage in heavy rain. But improvements were hard to quantify and similarly good results were achieved in the Netherlands by applying a simple Z–R relation together with a mean field bias-correction.

Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials

“…Only about 0.3 percentage points of the MAP underestimation can be attributed to the radar gaps due to the reduced radius, so that there is quite a systematic negative bias in the data. Most other studies on radar-based QPE evaluations also revealed underestimations of precipitation totals [9,[55][56][57][58], but a few authors reported an overestimation [56,59]. However, such results are hard to compare due to large differences in radar hardware, correction algorithms and evaluation methods in the studies.…”

Spatial Rainfall Variability in Urban Environments—High-Density Precipitation Measurements on a City-Scale

“…For modern applications, a combination of all three technologies (rain gauges, C-band, and X-band) would yield the best results. So far, however, cities like Aalborg, Denmark with the necessary infrastructure for such a combination are the exception [23,24].…”