Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Validation of Extreme Rainfall Estimation in an Urban Area derived from Satellite Data : A Case Study on the Heavy Rainfall Event in July, 2011

위성 자료를 이용한 도시지역 극치강우 모니터링: 2011년 7월 집중호우를 중심으로

  • Yoon, Sun-Kwon (Climate Change Research Team, Climate Research Department, APEC Climate Center) ;
  • Park, Kyung-Won (Climate Change Research Team, Climate Research Department, APEC Climate Center) ;
  • Kim, Jong Pil (Climate Change Research Team, Climate Research Department, APEC Climate Center) ;
  • Jung, Il-Won (Climate Change Research Team, Climate Research Department, APEC Climate Center)
  • 윤선권 (APEC 기후센터 연구본부 기후변화연구팀) ;
  • 박경원 (APEC 기후센터 연구본부 기후변화연구팀) ;
  • 김종필 (APEC 기후센터 연구본부 기후변화연구팀) ;
  • 정일원 (APEC 기후센터 연구본부 기후변화연구팀)
  • Received : 2014.02.10
  • Accepted : 2014.03.21
  • Published : 2014.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

본 논문에서는 천리안(Communication, Ocean and Meteorological Satellite; COMS)과 TRMM(Tropical Rainfall Measurement Mission)을 통하여 관측한 위성영상자료를 이용한 극치강우(Extreme Rainfall) 추정 알고리즘을 개발하였으며, 2011년 7월 집중호우를 대상으로 그 적용성을 평가하였다. TRMM/PR(TRMM/Precipitation Radar)과 AWS(Automatic Weather System) 자료를 이용하여 고도에 따른 멱급수 회귀방정식으로 Z-R관계식을 추정한 결과 $Z=303R^{0.72}$를 산출하였으며, 지상관측 자료와 비교한 결과 상관계수가 0.57로 분석되었다. 이 값과 TRMM/VIRS(TRMM/Visible Infrared Scanner)와의 관계를 이용하여 극치강우알고리즘을 개발하였으며, 천리안 위성에 적용하여 10분강 우를 추정한 결과 강우강도가 큰 경우에는 과소 추정하는 경향이, 작은 경우에는 과대 추정하는 경향이 있는 것으로 분석되었으나, 전반적인 패턴은 관측과 유사한 경향이 있는 것으로 분석되었다. 또한 이 알고리즘을 같은 센서를 이용하는 천리안 위성에 적용하여 AWS의 상관관계를 분석한 결과, 10분 강우량의 경우 상관계수는 0.517로 평균제곱근 오차는 3.146으로 분석되었고, 공간 상관행렬 오차의 평균은 -0.530~-0.228의 음의 상관을 보이는 것으로 분석되었다. 위성자료를 이용한 극치강우량 추정의 오차 발생 원인은 여러 가지 외부적인 요인으로 판단되며, 지속적인 알고리즘 개선 및 오차보정을 통한 정확도 개선이 필요한 것으로 사료된다. 본 연구의 결과는 추후 다양한 정지궤도위성의 이용을통 한 다중 원격탐사자료의 활용으로 보다 정확한 미계측 유역 수문자료 확충 및 실시간 홍수 예 경보 시스템 구축에 활용이 가능할 것으로 사료된다.

Keywords

References

  1. Baek, J.J., and Choi, M.H. (2012). "Available of land surface temperature from the COMS in the Korea Peninsula." Journal of Korean Water Resources Association, Vol. 45, No. 8, pp. 755-765. https://doi.org/10.3741/JKWRA.2012.45.8.755
  2. Cho, H.R., Hwang, S.H., Cho, Y.S., and Choi, M.H. (2013). "Analysis of spatial precipitation field using downscaling on the Korean Peninsula." J. Korea Water Resources Association, Vol. 46, No. 11, pp. 1129-1140. https://doi.org/10.3741/JKWRA.2013.46.11.1129
  3. Huffman, G.J., Adler, R.F., Bolvin, D.T., Gu, G., Nelkin, E.J., Bowman, K.P., Hong, Y.E., Stocker, F., and Wolff, D.B. (2007). "The TRMM Multi-satellite precipitation analysis: quasi-global, multi-year, combined-sensor precipitation estimates at fine scale." J. Hydrometeor, Vol. 8, No. 1, pp. 38-55. https://doi.org/10.1175/JHM560.1
  4. Kang, S.M., Shin, H.J., Kwon, H.J., and Kim, S.J. (2006). "Extraction of snowmelt factors using NOAA satellite images and meteorological data." Journal of Korea Water Resources Association, Vol. 39, No. 10, pp. 845-854. https://doi.org/10.3741/JKWRA.2006.39.10.845
  5. Kim, G., and Cho, S. (2012). "Summer precipitation forecast using satellite data and numerical weather forecast model data." Journal of Korea Water Resources Association, Vol. 45, No. 7, pp. 631-641. https://doi.org/10.3741/JKWRA.2012.45.7.631
  6. Kim, J.P., Park, K.W., Jung, I.W., Han, K.S., and Kim, G. (2013). "Application of high resolution multi-satellite precipitation products and a distributed hydrological modeling for daily runoff simulation." Korean Journal of Remote Sensing, Vol. 29, No. 2, pp. 263-274. https://doi.org/10.7780/kjrs.2013.29.2.10
  7. Kozu, T., and Iguchi, T. (1996). "A preliminary study of non-uniform bean filling sorrection for spaceborne radar rainfall measurement." IEICE Trans Commun, E79-B, pp. 763-769.
  8. Kummerow, C., Barnes, W., Kozu, T., Shiue, L., and Simpson, J. (1998). "The Tropical Rainfall Measurement Mission (TRMM) Sensor Package." J. Atmos. and Ocean Tech., Vol. 15, pp. 808-816.
  9. Lee, B.J., Bae, D.H., and Jeong, C.S. (2003). "Runoff curve number estimation for cover and treatment classification of satellite image (II): Application and verification." Journal ofKorea Water Resources Association, Vol. 36, No. 6, pp. 999-1012. https://doi.org/10.3741/JKWRA.2003.36.6.999
  10. NASA. (2014). National Aeronautics and Space Adminstration, URL: http://pmm.nasa.gov/data-access/, accessed 2014.
  11. NMSC. (2014). National Meteorological Satellite Center, URL: http://nmsc.kma.go.kr/, accessed 2014.
  12. Park, K.W. (2012). The Development of Precipitation Retrieval Algorithm Using multi-Satellites. APCC Technical Report 2012-04, pp. 1-31.
  13. WAMIS. (2014). Water Management Information System, URL: www.wamis.go.kr, accessed 2014.