Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 3.089 IF 3.089
  • IF 5-year<br/> value: 3.700 IF 5-year
  • CiteScore<br/> value: 3.59 CiteScore
  • SNIP value: 1.273 SNIP 1.273
  • SJR value: 2.026 SJR 2.026
  • IPP value: 3.082 IPP 3.082
  • h5-index value: 45 h5-index 45
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
12 Dec 2016
Review status
This discussion paper is a preprint. A revision of the manuscript for further review has not been submitted.
Exploring the potential of utilizing high resolution X-band radar for urban rainfall estimation
Wen-Yu Yang1, Guang-Heng Ni1, You-Cun Qi2,3, Yang Hong1,4, and Ting Sun1 1State Key Laboratory of Hydro - Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
2Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma
3NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
4Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma
Abstract. X-band-radar-based quantitative precipitation estimation (QPE) system is increasingly gaining interest thanks to its strength in providing high spatial resolution rainfall information for urban hydrological applications. However, prior to such applications, a variety of errors associated with X-band radars are mandatory to be corrected. In general, X-band radar QPE systems are affected by two types of errors: 1) common errors (e.g. mis-calibration, beam blockage, attenuation, non-precipitation clutter, variations in the raindrop size distribution) and 2) “wind drift” errors resulting from non-vertical falling of raindrops. In this study, we first assess the impacts of different corrections of common error using a dataset consisting of one-year reflectivity observations collected at an X-band radar site and a distrometer along with rainfall observations in Beijing urban area. The common error corrections demonstrate promising improvements in the rainfall estimates, even though an underestimate of 24.6% by the radar QPE system in the total accumulated rainfall still exists as compared with gauge observations. The most significant improvement is realized by beam integration correction. The DSD-related corrections (i.e., convective–stratiform classification and local Z-R relationship) also lead to remarkable improvement and highlight the necessity of deriving the localized Z-R relationships for specific rainfall systems. The effectiveness of wind drift correction is then evaluated for a fast-moving case, whose results indicate both the total accumulation and the temporal characteristics of the rainfall estimates can be improved. In conclusion, considerable potential of X-band radar in high-resolution rainfall estimation can be realized by necessary error corrections.
Citation: Yang, W.-Y., Ni, G.-H., Qi, Y.-C., Hong, Y., and Sun, T.: Exploring the potential of utilizing high resolution X-band radar for urban rainfall estimation, Atmos. Meas. Tech. Discuss.,, in review, 2016.
Wen-Yu Yang et al.
Wen-Yu Yang et al.


Total article views: 350 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
252 67 31 350 21 29

Views and downloads (calculated since 12 Dec 2016)

Cumulative views and downloads (calculated since 12 Dec 2016)

Viewed (geographical distribution)

Total article views: 349 (including HTML, PDF, and XML)

Thereof 349 with geography defined and 0 with unknown origin.

Country # Views %
  • 1



Latest update: 21 Apr 2018
Publications Copernicus
Short summary
Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found that error corrections greatly improve X-band-radar-based rainfall estimation. Specifically, the greatest improvement is realized by the beam integration. Derivation of localized Z-R relationships for specific rainfall systems is also of great importance. Moreover, wind drift correction improves quantitative estimates and temporal consistency.
Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found...