Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

Details

• Journal Title:
  Journal of Hydrometeorology
• Personal Author:
  Huang, Hao ; Zhao, Kun ; Chen, Haonan ; Hu, Dongming ; Fu, Peiling ; Lin, Qing ; Yang, Zhengwei
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; CIRA (Cooperative Institute for Research in the Atmosphere) ; ESRL (Earth System Research Laboratory)
• Description:
  The attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.
• Keywords:
  Atmospheric Science
• Source:
  Journal of Hydrometeorology, 21(7), 1605-1620
• DOI:
  https://doi.org/10.1175/jhm-d-19-0265.1
• ISSN:
  1525-755X ; 1525-7541
• Format:
  PDF
• Publisher:
  American Meteorological Society
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:17ee214f752046f7831cbf8057a120719d231c68a2651f662229da0b2dbff1ae
• Download URL:
  https://repository.library.noaa.gov/view/noaa/53481/noaa_53481_DS1.pdf
• File Type:
  [PDF - 7.94 MB ]