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High-Resolution QPE System for Taiwan

  • Jian Zhang
  • Kenneth Howard
  • Pao-Liang Chang
  • Paul Tai-Kuang Chiu
  • Chia-Rong Chen
  • Carrie Langston
  • Wenwu Xia
  • Brian Kaney
  • Pin-Fang Lin

Abstract

Over the last five years the Central Weather Bureau of Taiwan and the United States NOAA/National Severe Storms Laboratory have been involved in a research and development initiative to improve the monitoring and prediction of flash floods, debris flows, and severe storms for the Taiwan environment. The initiative has produced a system that integrates observations from weather radars, rain gauges, satellites, and numerical weather prediction model fields to produce high resolution (1 km to 500 m) and rapid update (10-min) rainfall and severe storm monitoring products. These prototype products are assessed for potential use by government agencies and emergency managers for flood, flash flood, and mudslide warnings and water resource managements. The system also facilitates collaborations with academic communities for research and development of radar applications including QPE and nowcasting. This paper overviews the system structure and products, the research activities supporting the system, and the challenges faced in producing high resolution, accurate QPE for Taiwan.

Keywords

Numerical Weather Prediction Model Severe Storm Weather Radar Precipitation Type Central Weather Bureau 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Amburn, SA, Wolf PL (1997) VIL density as a hail indicator. Wea Forecast 12, pp 473–478CrossRefGoogle Scholar
  2. Fulton R, Breidenbach J, Seo D-J, Miller D, O’Bannon T (1998) The WSR-88D rainfall algorithm. Wea Forecast 13, pp 377–395CrossRefGoogle Scholar
  3. Greene DR, Clark RA (1972) Vertically integrated liquid water—a new analysis tool. Mon Wea Rev 100, pp 548–552.CrossRefGoogle Scholar
  4. Lakshmanan V, Fritz A, Smith T, Hondl K, Stumpf GJ (2007) An automated technique to quality control radar reflectivity data. J Appl Meteor 46, 288–305CrossRefGoogle Scholar
  5. Lakshmanan V, Smith T, Hondl K, Stumpf GJ, Witt A (2006) A real-time, three dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity and derived products. Wea Forecast 21, 802–823CrossRefGoogle Scholar
  6. Witt A, Eilts MD, Stumpf GJ, Johnson JT, Mitchell ED, Thomas KW (1998) An enhanced hail detection algorithm for the WSR-88D. Wea Forecast 13, pp 286–303CrossRefGoogle Scholar
  7. Xu X, Howard K, Zhang J (2008) An automated radar technique for the identification of tropical precipitation. J Hydrometiorol, accepted, doi: 10.1175/2007 JHM954.1.Google Scholar
  8. Zhang J, Langston C, Howard K (2008) Bright band identification based on vertical profiles of reflectivity from the WSR-88D. J Atmos Ocean Tech 25, 1859–1872.CrossRefGoogle Scholar
  9. Zhang J, Howard K, Gourley JJ (2005) Constructing three-dimensional multiple radar reflectivity mosaics: Examples of convective storms and stratiform rain echoes. J Atmos Ocean Tech 22, 30–42CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jian Zhang
    • 1
  • Kenneth Howard
  • Pao-Liang Chang
  • Paul Tai-Kuang Chiu
  • Chia-Rong Chen
  • Carrie Langston
  • Wenwu Xia
  • Brian Kaney
  • Pin-Fang Lin
  1. 1.NOAA/OAR National Severe Storms Laboratory and Cooperative Institute for Mesoscale Meteorological StudiesThe University of OklahomaNormanUSA

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