DOI QR코드

DOI QR Code

A Multi-sensor basedVery Short-term Rainfall Forecasting using Radar and Satellite Data - A Case Study of the Busan and Gyeongnam Extreme Rainfall in August, 2014-

레이더-위성자료 이용 다중센서 기반 초단기 강우예측 - 2014년 8월 부산·경남 폭우사례를 중심으로 -

  • 장상민 (APEC기후센터, 연구본부, 기후연구변화팀) ;
  • 박경원 (APEC기후센터, 연구본부, 기후연구변화팀) ;
  • 윤선권 (APEC기후센터, 연구본부, 기후연구변화팀)
  • Received : 2016.03.22
  • Accepted : 2016.04.15
  • Published : 2016.04.30

Abstract

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.

본 연구에서는 2014년 8월 부산 경남 집중호우 사례를 대상으로 레이더와 위성결합 Multi-sensor Blending 초단기 강우예측을 실시하였다. 레이더 최적 Z-R관계는 열대형 강수 Z-R관계식($Z=32R^{1.65}$)을 적용하였으며, 20 mm/h 이상의 강한 강우에서 강수량 추정 정확도가 향상됨을 확인하였다. 또한 60 mm/h 이상 강한 폭우사상에 대하여 천리안 위성자료와 레이더자료를 합성한 결과 정량강수 추정 성능이 향상됨을 확인하였다. 지속시간별 강우예측 정확도 검증을 위하여 AWS, MAPLE 자료와 비교결과, 강우예측 1시간까지 약 50%이상의 지점강우예측 정확도를 확보하였으며, 10분 단위 예측시간별 상관계수는 0.80~0.53, 평균제곱근오차는 3.99~6.43 mm/h로 분석되었다. 본 연구 결과 레이더와 위성정보를 이용한 보다 신뢰성 있는 강우예측 정보 활용이 가능할 것으로 판단되며, 향후 지속적인 사례연구와 레이더 위성 활용 정량강수량 추정 및 예측, 그리고 위성강수 추정 알고리즘 개선의 노력이 필요하다.

Keywords

References

  1. Atlas, D., C. Ulbrich, F.D. Jr. Marks, E. Amitai, and C.R. Williams 1999. Systematic variation of drop size and radar-rainfall relations, Journal of Geophysical Research, 104: 6155-6169. https://doi.org/10.1029/1998JD200098
  2. Blanchard, D.C. 1953. Raindrop size distribution in Hawaiian rains, Journal of Meteorology, 10: 457-473. https://doi.org/10.1175/1520-0469(1953)010<0457:RSDIHR>2.0.CO;2
  3. Calheiros, R.V. and I. Zawadzk 1987. Reflectivity-rain rate relationships for radar hydrology in Brazil, Journal of Climate Applied Meteorology, 26: 118-132. https://doi.org/10.1175/1520-0450(1987)026<0118:RRRRFR>2.0.CO;2
  4. Chandrasekhar, V. and R. Cifelli 2012. Concepts and principles of rainfall estimation from radar: Multi sensor environment and data fusion, Indian Journal of Radio and Space Physics, 41: 389-402.
  5. Fulton, R. A., Breidenbach, J. P., Seo, D. J., Miller, D. A., and O'Bannon, T. (1998). The WSR-88D rainfall algorithm, Weather and Forecasting, 13(2): 377-395. https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2
  6. Germann, U. and I. Zawadzki, 2002. Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology, Monthly Weather Review, 130(12): 2859-2873. https://doi.org/10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2
  7. Gochis, D., R. Schumacher, K. Friedrich, N. Doesken, M. Kelsch, J. 4 Sun, K. Ikeda, D.T. Lindsey, A. Wood, B. Dolan, S. Matrosov, A. Newman, K. Mahoney, S. Rutledge, R. Johnson, P. Kucera, P. Kennedy, D. Sempere-Torres, M. Steiner, R. Roberts, J. Wilson, W. Yu, V. Chandrasekar, R. Rasmussen, A. Anderson, B. Brown 2015. The great Colorado flood of September 2013, Bulletin of the American Meteorological Society, 96(9): 1461-1487. https://doi.org/10.1175/BAMS-D-13-00241.1
  8. Gourley, J.J. and R.A. Maddox 2002. An exploratory multisensor technique for quantitative estimation of stratiform rainfall, Journal of Hydrolometeorology, 3: 166-180. https://doi.org/10.1175/1525-7541(2002)003<0166:AEMTFQ>2.0.CO;2
  9. Jones, D.M.A. 1956. Rainfall drop size distribution and radar reflectivity, Research Report, No. 6, U.S. Army Contract DA-36-039 SC-64723, Illinois State Water Survey, Urbanba, Vol. 20, pp. 1-20.
  10. Kim J.P., S.K. Yoon, G.S. Kim, and Y.I. Moon 2015. Application of very short-term rainfall forecasting to using water simulation using TREC method, Journal of Korea Water Resources Association, 48(5): 409-423. (Korean with English abstract) https://doi.org/10.3741/JKWRA.2015.48.5.409
  11. Kitzmiller, D., D. Miller, R. Fulton, and R. Ding 2013. Radar and multisensor precipitation estimation techniques in National Weather Service hydrologic operations, Journal of Hydrologic Engineering, 18(2): 133-142. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000523
  12. Marshall, J.S., and W.M.K. Palmer 1948. The distribution of raindrops with size, Journal of Meteorology, 5(4): 165-166. https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2
  13. Ng, C.W., and H. Y. Yeung 2013. Development of Radar-Satellite Blended QPE Technique and Application to Rainfall Nowcasting, Proc. of 27th Guangdong-Hong Kong-Macao Seminar, Reprint 1058, pp. 1-20.
  14. NMSC (National Meteorological Satellite Center), (2016). http://nmsc.kma.go.kr/, Accessed Jan. 2016.
  15. NIMS (National Institute of Meteorological Sciences)(2003). Application of the Z-R relationshiprespect with the Type of prediction using radar of Jindo, National Institute of MeteorologicalSciences in Gwangju, KMA [C1-7], pp. 1-83.(in Korean)
  16. Park, K.W. 2015. Regional Impacts of Hydrometeorological variability by extreme climate pattern changes and its heavy rainfall short-term forecast system over the Korean Peninsula. APCC Research Report, 2015-15, pp.1-86. (Korean with English abstract)
  17. Rinehart, R.E. and T. Garvey 1978. Three dimensional storm motion detection by conventional weather radar, Nature, 273: 287-289. https://doi.org/10.1038/273287a0
  18. Steiner, M., and R.A. Houze Jr., 1993. Three-dimensional validation at TRMM ground truthsites: some early results from Darwin, Australia, Proc. of 26th International Conference on Radar Meteorology, Norman, OK, May. 24-28,pp. 447-450.
  19. Vasiloff, S.V., D.J. Seo, K.W. Howard, J. Zhang, D.H.Kitzmiller, M.G. Mullusky, W. F. Krajewski,E.A. Brandes, R.M. Rabin, D Berkowitz, H.E. Brooks, J.A. McGinley, R.J. Kuligowski, and B.G. Brown (2007). Improving QPE and very short term QPF. Bulletin of the American Meteorological Society, 88(12): 1899-1911. https://doi.org/10.1175/BAMS-88-12-1899
  20. Wetchayont, P., T. Hayasaka, T. Satomura, S. Katagiri,and S. Baimoung 2013. Retrieval of rainfall by combining rain gauge, ground-based radar and satellite measurements over Phimai, Thailand, Scientific Online Letters on the Atmosphere, 9:166-169.
  21. Yoon, S.K., and Moon, Y.I. 2014. The Recent Increasing Trends of Exceedance Rainfall Thresholds Over the Korean Major Cities, Journal of the Korean Society of Civil Engineers,34(1): 117-133. (Korean with English abstract) https://doi.org/10.12652/Ksce.2014.34.1.0117
  22. Zhang, J. and Y. Qi 2010. A real-time algorithm for the correction of bright band effects in radar-derived precipitation estimation, Journal ofHydrometeorology, 11: 1157-1171. https://doi.org/10.1175/2010JHM1201.1
  23. Zhang, J., K. Howard, and co-authors 2011. National Mosaic and multi-sensor QPE (NMQ) system: description, results and future plans, Bulletin of the American Meteorological Society, 92: 1321-1338. https://doi.org/10.1175/2011BAMS-D-11-00047.1

Cited by

  1. Verification of Initial Field of Very Short-Term Radar Rainfall Forecasts using Advanced System: A Case Study of Typhoon CHABA in 2016 vol.18, pp.3, 2018, https://doi.org/10.9798/KOSHAM.2018.18.3.57
  2. Evaluating the effect of grid size and type in integrated 1D/2D coupled urban inundation modelling on the interacting discharge between the surface and sewerage system vol.12, pp.suppl1, 2019, https://doi.org/10.1111/jfr3.12537
  3. 천리안 위성과 GPM 위성을 활용한 한반도 호우사상 강우추정 기술 개발 vol.35, pp.5, 2016, https://doi.org/10.7780/kjrs.2019.35.5.2.9