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Korean Meteorological Society (한국기상학회)
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Development of Radar-Based Multi-Sensor Quantitative Precipitation Estimation Technique

레이더기반 다중센서활용 강수추정기술의 개발

  • Lee, Jae-Kyoung (Korea Meteorological Administration, Weather Radar Center) ;
  • Kim, Ji-Hyeon (Korea Meteorological Administration, Weather Radar Center) ;
  • Park, Hye-Sook (Korea Meteorological Administration, Weather Radar Center) ;
  • Suk, Mi-Kyung (Korea Meteorological Administration, Weather Radar Center)
  • 이재경 (기상청 기상레이더센터 레이더분석과) ;
  • 김지현 (기상청 기상레이더센터 레이더분석과) ;
  • 박혜숙 (기상청 기상레이더센터 레이더분석과) ;
  • 석미경 (기상청 기상레이더센터 레이더분석과)
  • Received : 2014.05.19
  • Accepted : 2014.08.22
  • Published : 2014.09.30
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Abstract

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

Keywords

References

  1. Cha, J.-W., J.-K. Lee, H.-S. Park, J.-S. Park, and K.-H. Kim, 2013: Improvement of the Radar-AWS Rainrate calculation system using the Local Gauge Correction (LGC) method. Proceedings of the 2013 Spring Meeting of Korea Meteorological Society, 104-105.
  2. Chung, C.-K., M.-K. Suk, H.-K. Kim, K.-H. Jang, and Y.-J. Choi, 2007: Improvement of the Radar-AWS Rainrate calculation system using the dual fit method, Proceedings of the 2007 Autumn Meeting of Korea Meteorological Society, 114-115
  3. Fulton, R. A., J. P. Breidenbach, D.-J. Seo, D. Miller, and T. O'Bannon, 1998: The WSR-88D rainfall algorithm. Wea. Forecasting, 13, 377-395. https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2
  4. Korea Meteorological Administration, 2012: Development of radar-based multi-sensors quantitative precipitation estimation technical report. Weather Radar Center.
  5. Lakshmanan, V., J. Zhang, and K. Howard, 2010: A technique to censor biological echoes in weather radar images. J. Appl. Meteor. Climatol., 49, 453-462. https://doi.org/10.1175/2009JAMC2255.1
  6. Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, and G. J. Stumpf, 2007: An automated technique to quality control radar reflectivity data. J. Appl. Meteor. Climatol., 46, 288-305. https://doi.org/10.1175/JAM2460.1
  7. Lee, J.-K., J.-W. Cha, D.-S. Kim, and J.-S. Park, 2013: Accuracy estimation of the radar rainfall using local gauge correction method, Proceeding of the 2013 Korea Society of Civil Engineers, 1757-1760.
  8. Lee, J.-H., and C.-S. Ryu, 2009: Radar meteorology. Sigma Press, 101 pp.
  9. Maddox, R., J. Zhang, J. J. Gourley, and K. Howard, 2002: Weather radar coverage over the contiguous United States. Wea. Forecasting, 17, 927-934. https://doi.org/10.1175/1520-0434(2002)017<0927:WRCOTC>2.0.CO;2
  10. Marshall, J. S., W. Hitschfeld, and K. L. S. Gunn, 1955: Advances in radar weather. Adv. Geophys., 2, 1-56.
  11. O'Bannon, T. 1997: Using a 'terrain-based' hybrid scan to improve WSR-88D precipitation estimates. Proceeding of 28th Conference on Radar Meteorology, Austin, TX, American Meteorology, 506-507.
  12. Radar operation center, 1999: Guidance on selecting Z-R relationships. Radar operation center.
  13. Rosenfeld, D., D. B. Wolff, and E. Amitai, 1994: The window probability matching method for rainfall measurements with radar. J. Appl. Meteorology, 33, 682- 693. https://doi.org/10.1175/1520-0450(1994)033<0682:TWPMMF>2.0.CO;2
  14. Rosenfeld, D., D. B. Wolff, and D. Altas, 1993: General probability- matched relations between radar reflectivity and rain rate. Journal of Applied Meteorol., 32, 50-72. https://doi.org/10.1175/1520-0450(1993)032<0050:GPMRBR>2.0.CO;2
  15. Ware, E. C., 2005: Corrections to radar-estimated precipitation using observed rain gauge data. M. S. thesis, Cornell University.
  16. Xu, X., J. Howard, and J. Zhang, 2008: An automated radar technique for the identification of tropical precipitation. J. Hydrometeor., 9, 885-902. https://doi.org/10.1175/2007JHM954.1
  17. Zhang, J., and Y. Qi, 2010: A real-time algorithm for the correction of brightband effects in radar derived precipitation estimation. J. Hydrometer., 11, 1157-1171. https://doi.org/10.1175/2010JHM1201.1
  18. Zhang, J., C. Langston, and K. Howard, 2008: Brightband identification based on vertical profiles of reflectivity from the WSR-88D. J. Atmos. Oceanic Technol., 25, 1859-1872. https://doi.org/10.1175/2008JTECHA1039.1
  19. Zhang, J., and Coauthors, 2011: National mosaic and multisensor QPE(NMQ) system: Desscription, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321-1338. https://doi.org/10.1175/2011BAMS-D-11-00047.1

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