Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
Discussion papers
https://doi.org/10.5194/hess-2019-427
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-427
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 15 Aug 2019

Submitted as: research article | 15 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

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

Marc Schleiss1, Jonas Olsson2, Peter Berg2, Tero Niemi3,5, Teemu Kokkonen3, Søren Thorndahl4, Rasmus Nielsen4, Jesper Ellerbæk Nielsen4, Denica Bozhinova2, and Seppo Pulkkinen5 Marc Schleiss et al.
  • 1Dept. of Geoscience and Remote Sensing, Delft University of Technology, Netherlands
  • 2Dept. of Hydrology, Swedish Meteorological and Hydrological Institute SMHI, Norrkoping, Sweden
  • 3Dept. of Built Environment, Aalto University, Finland
  • 4Dept. of Civil Engineering, Aalborg University, Denmark
  • 5Finnish Meteorological Institute FMI, Helsinki, Finland

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.

Marc Schleiss et al.
Interactive discussion
Status: open (until 10 Oct 2019)
Status: open (until 10 Oct 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Marc Schleiss et al.
Marc Schleiss et al.
Viewed  
Total article views: 215 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
154 57 4 215 6 7
  • HTML: 154
  • PDF: 57
  • XML: 4
  • Total: 215
  • BibTeX: 6
  • EndNote: 7
Views and downloads (calculated since 15 Aug 2019)
Cumulative views and downloads (calculated since 15 Aug 2019)
Viewed (geographical distribution)  
Total article views: 123 (including HTML, PDF, and XML) Thereof 121 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 19 Sep 2019
Publications Copernicus
Download
Short summary
The accuracy of weather radar during heavy rain is investigated. The work is performed within the context of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and flooding. Results show that radars underestimate rainfall peaks by up to 66 % compared with gauges. Higher resolution products have lower biases but for capturing the peaks, the ability to combine multiple overlapping measurements seems to be more important than resolution.
The accuracy of weather radar during heavy rain is investigated. The work is performed within...
Citation