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Estimation of reflectivity-rainfall relationship parameters and uncertainty assessment for high resolution rainfall information

고해상도 강수정보 생산을 위한 레이더 반사도-강수량 관계식 매개변수 보정 및 불확실성 평가

  • Kim, Tae-Jeong (Planning & Management Division, Korea Institute of Hydrological Survey) ;
  • Kim, Jang-Gyeong (Bayesianworks Research Institute) ;
  • Kim, Jin-Guk (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kwon, Hyun-Han (Department of Civil & Environmental Engineering, Sejong University)
  • 김태정 (한국수자원조사기술원 전략기획실) ;
  • 김장경 (베이지안웍스) ;
  • 김진국 (한국건설기술연구원 국토보전연구본구) ;
  • 권현한 (세종대학교 공과대학 건설환경공학과)
  • Received : 2021.03.12
  • Accepted : 2021.04.05
  • Published : 2021.05.31

Abstract

A fixed reflectivity-rainfall relationship approach, such as the Marshall-Palmer relationship, for an entire year and different seasons, can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of long-term radar reflectivity for South Korea to obtain a nationwide calibrated Z-R relationship and the associated uncertainties within a Bayesian inference framework. A calibrated spatially structured pattern in the parameters exists, particularly for the wet season and parameter for the dry season. A pronounced region of high values during the wet and dry seasons may be partially associated with storm movements in that season. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields. In contrast, the radar rainfall fields obtained from the existing Marshall-Palmer relationship show a systematic underestimation. In the event of high impact weather, it is expected that the value of national radar resources can be improved by establishing an active watershed-level hydrological analysis system.

일반적으로 레이더 강수량 추정에 활용되는 Marshall-Palmer 관계식은 강수현상의 계절적 변동성을 고려하지 않고 선별된 강수사상에 대하여 시공간적으로 고정된 매개변수를 적용하여 레이더 강수량을 추정하므로 실제 강수량과 추정된 레이더 강수량은 정량적인 오차가 발생할 수 있다. 이러한 제약성을 극복하고자 본 연구는 장기간 레이더 반사도 인자를 가용하여 레이더 반사도-강수량 관계식 매개변수를 Bayesian 추론기법으로 보정하고 불확실성을 정량화하여 레이더 강수량의 편의 보정을 수행하였다. Bayesian 추론기법 기반으로 추정된 레이더 반사도-강수량 관계식의 보정 매개변수는 계절성이 규명되었으며 지역적 특성이 존재하였다. Bayesian 추론기법을 통하여 산정된 레이더 강수량은 Marshall-Palmer 관계식의 과소추정 문제를 극복하고 지상 강수특성을 정량적으로 현실성 있게 재현하였다. 본 연구결과는 집중호우 발생 시 능동적인 유역단위 수자원 해석 시스템을 구축하여 국가적 레이더 자원의 가치를 향상할 수 있을 것으로 판단된다.

Keywords

Acknowledgement

본 연구는 환경부/한국환경산업기술원의 지원(과제번호 127568)으로 수행되었습니다.

References

  1. Ajayi, G.O., and Owolabi, I.E. (1987). "Rainfall parameters from disdrometer dropsize measurements at a tropical station." Annals of Telecommunications, Vol. 42, pp. 3-12. https://doi.org/10.1007/BF02996163
  2. Alfieri, L., Claps, P., and Laio, F. (2010). "Time-dependent ZR relationships for estimating rainfall fields from radar measurements." Natural Hazards and Earth System Sciences, Vol. 10, No. 1, p. 149. https://doi.org/10.5194/nhess-10-149-2010
  3. Ayo, A.O., Ojo, J.S., and Ajewole, M.O. (2015). "Systematic variation of rain rate and radar reflectivity relations for micro wave applications in a tropical location." IOSR Journal of Applied Physics, Vol. 7, No. 6, pp. 23-29.
  4. Blanchard, D.C. (1953). "Raindrop size-distribution in Hawaiian rains." Journal of Meteorology, Vol. 10, No. 6, pp. 457-473. https://doi.org/10.1175/1520-0469(1953)010<0457:RSDIHR>2.0.CO;2
  5. Carlson, P.E., and Marshall, J.S. (1972). "Measurement of snowfall by radar." Journal of Applied Meteorology, Vol. 11, No. 3, pp. 494-500. https://doi.org/10.1175/1520-0450(1972)011<0494:MOSBR>2.0.CO;2
  6. Chen, F., Gao, Y., Wang, Y., and Li, X. (2020). "A downscalingmerging method for high-resolution daily precipitation estimation." Journal of Hydrology, Vol. 581, 124414. https://doi.org/10.1016/j.jhydrol.2019.124414
  7. Chumchean, S., Sharma, A., and Seed, A. (2006). "An integrated approach to error correction for real-time radar-rainfall estimation." Journal of Atmospheric and Oceanic Technology, Vol. 23, No. 1, pp. 67-79. https://doi.org/10.1175/JTECH1832.1
  8. Das, M.K., Chowdhury, M.A.M., Das, S., Debsarma, S.K., and Kar-makar, S. (2015). "Assimilation of Doppler weather radar data and their impacts on the simulation of squall events during pre-monsoon season." Natural Hazards, Vol. 77, No. 2, pp. 901-931. https://doi.org/10.1007/s11069-015-1634-9
  9. 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, Vol. 13, No. 2, pp. 377-395. https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2
  10. Hasan, M.M., Sharma, A., Mariethoz, G., Johnson, F., and Seed, A. (2016). "Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework." Advances in Water Resources, Vol. 97, pp. 205-218. https://doi.org/10.1016/j.advwatres.2016.09.011
  11. Hubbert, J.C., Dixon, M., and Ellis, S.M. (2009). "Weather radar ground clutter. Part II: Real-time identification and filtering." Journal of Atmospheric and Oceanic Technology, Vol. 26, No. 7, pp. 1181-1197. https://doi.org/10.1175/2009JTECHA1160.1
  12. Jones, D.M.A. (1956). Rainfall drop size-distribution and radar reflectivity. ISWS Contract Report CR 009, Illinois State Water Survey, IL, U.S.
  13. Jorgensen, D.P., and Willis, P.T. (1982). "A ZR relationship for hurricanes." Journal of Applied Meteorology, Vol. 21, No. 3, pp. 356-366. https://doi.org/10.1175/1520-0450(1982)021<0356:AZRRFH>2.0.CO;2
  14. Joss, J., Schram, K., Thams, J.C., and Waldvogel, A. (1970). On the quantitative determination of precipitation by a radar. Issue 63 of Wissenschaftliche Mitteilung, DLA, U.S.
  15. Kessinger, C., Ellis, S., and Van Andel, J. (2003). "The radar echo classifier: A fuzzy logic algorithm for the WSR-88D." Preprints-CD, 3rd Conference on Artificial Applications to the Environmental Science, AMS, VA, U.S.
  16. Kim, J.H., and Yoo, C.S. (2014). "Using extended Kalman filter for real-time decision of parameters of Z-R relationship." Journal of Korea Water Resources Association, Vol. 47, No. 2, pp. 119-133. https://doi.org/10.3741/JKWRA.2014.47.2.119
  17. Kim, J.H., Lee, K.D., and Bae, D.H. (2005). "Hydrologic utilization of radar-derived rainfall (II) uncertainty analysis." Journal of Korea Water Resources Association, Vol. 38, No. 12, pp. 1051-1060. https://doi.org/10.3741/JKWRA.2005.38.12.1051
  18. Kim, T.J., Kwon, H.H., and Kim, K.B. (2021). "Calibration of the reflectivity-rainfall rate (ZR) relationship using long-term radar reflectivity factor over the entire South Korea region in a Bayesian perspective." Journal of Hydrology, Vol. 593.
  19. Kim, T.J., Park, M.H., and Kwon, H.H. (2018). "Quantitative evaluation of radar reflectivity and rainfall intensity relationship parameters uncertainty using Bayesian inference technique." Journal of Korea Water Resources Association, Vol. 51, No. 9, pp. 813-826. https://doi.org/10.3741/JKWRA.2018.51.9.813
  20. Kumar, L.S., Lee, Y.H., Yeo, J.X., and Ong, J.T. (2011). "Tropical rain classification and estimation of rain from ZR (reflectivityrain rate) relationships." Progress In Electromagnetics Research, Vol. 32, pp. 107-127. https://doi.org/10.2528/PIERB11040402
  21. Kusiak, A., Wei, X., Verma, A.P., and Roz, E. (2012). "Modeling and prediction of rainfall using radar reflectivity data: a data-mining approach." IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 4, pp. 2337-2342. https://doi.org/10.1109/TGRS.2012.2210429
  22. Kwon, H.H., Kim, J.G., Lee, J.S., and Na, B.K. (2012). "Uncertainty assessment of single event rainfall-runoff model using bayesian model." Journal of Korea Water Resources Association, Vol. 45, No. 5, pp. 505-516. https://doi.org/10.3741/JKWRA.2012.45.5.505
  23. Kwon, H.H., Lall, U., and Kim, S.J. (2016). "The unusual 2013-2015 drought in South Korea in the context of a multicentury precipitation record: Inferences from a nonstationary, multivariate, Bayesian copula model." Geophysical Research Letters, Vol. 43, No .16, pp. 8534-8544. https://doi.org/10.1002/2016GL070270
  24. Lee, J.K., Kim, J.H., and Suk, M.K. (2015). "Application of bias correction methods to improve the accuracy of quantitative radar rainfall in Korea." Atmospheric Measurement Techniques Discussions, Vol. 8, No. 4, pp. 4011-4047. https://doi.org/10.5194/amtd-8-4011-2015
  25. Lee, M.H., and Bae, D.H. (2018). "Uncertainty assessment of future projections on water resources according to climate downscaling and hydrological models." Journal of Hydroinformatics, Vol. 20, No. 3, pp. 597-607. https://doi.org/10.2166/hydro.2018.132
  26. Marshall, J.S., and Palmer, W.M. (1948). "The distribution of raindrops with size." Journal of Meteorology, Vol. 5, pp. 165-166. https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2
  27. Moon, J.W. (2019). "Estimation of Han River runoff using Cheugugi data." Journal of Korea Water Resources Association, Vol. 52, No. 12, pp. 1067-1074. https://doi.org/10.3741/JKWRA.2019.52.12.1067
  28. Moser, B.A., Gallus Jr, W.A., and Mantilla, R. (2015). "An initial assessment of radar data assimilation on warm season rainfall forecasts for use in hydrologic models." Weather and Forecasting, Vol. 30, No. 6, pp. 1491-1520. https://doi.org/10.1175/WAF-D-14-00125.1
  29. Noh, H.S., Lee, D.R., Hwang, S.H., and Kang, N.R. (2019). "Evaluation of the uncertainty level in radar-rainfall technology in Korea: Focus on comparative analysis with the united states." Journal of the Korean Society of Hazard Mitigation, Vol. 19, No. 1, pp. 35-44. https://doi.org/10.9798/kosham.2019.19.1.35
  30. Ohn, I.S., Kim, S.H., Seo, S.B., Kim, Y.O., and Kim, Y.D. (2020). "Bayesian uncertainty decomposition for hydrological projections." Journal of the Korean Statistical Society, Vol. 49, pp. 953-975. https://doi.org/10.1007/s42952-019-00042-8
  31. Probert-Jones, J.R. (1962). "The radar equation in meteorology." Quarterly Journal of the Royal Meteorological Society, Vol. 88, No. 378, pp. 485-495. https://doi.org/10.1002/qj.49708837810
  32. Rafieeinasab, A., Norouzi, A., Seo, D.J., and Nelson, B. (2015). "Improving high-resolution quantitative precipitation estimation via fusion of multiple radar-based precipitation products." Journal of Hydrology, Vol. 531, pp. 320-336. https://doi.org/10.1016/j.jhydrol.2015.04.066
  33. Ro, Y.H., and Yoo, C.S. (2014). "Applicability evaluation of probability matching method for parameter estimation of radar rain rate equation." Journal of The Korean Society of Civil Engineers, Vol. 34, No. 6, pp. 1765-1777. https://doi.org/10.12652/Ksce.2014.34.6.1765
  34. Rui, X.P., Qu, X.K., Yu, X.T., Lei, Q.L., and Fan, Y.L. (2019). "Quantitative rainfall estimation using weather radar based on the improved Kalman filter method." Applied Ecology and Environmental Research, Vol. 17, No. 1, pp. 369-381. https://doi.org/10.15666/aeer/1701_369381
  35. Sikorska, A.E., and Seibert, J. (2018). "Value of different precipitation data for flood prediction in an alpine catchment: A Bayesian approach." Journal of Hydrology, Vol. 556, pp. 961-971. https://doi.org/10.1016/j.jhydrol.2016.06.031
  36. Suk, M.K., Chang, K.H., Cha, J.W., and Kim, K.E. (2013). "Operational real-time adjustment of radar rainfall estimation over the South Korea region." Journal of the Meteorological Society of Japan. Series II, Vol. 91, No. 4, pp. 545-554. https://doi.org/10.2151/jmsj.2013-409
  37. Wang, J., and Wolff, D.B. (2010). "Evaluation of TRMM ground-validation radar-rain errors using rain gauge measurements." Journal of Applied Meteorology and Climatology, Vol. 49, No. 2, pp. 310-324. https://doi.org/10.1175/2009JAMC2264.1
  38. Yoo, C.S. (2002). "Rainfall seasonality and estimation errors of area-verage rainfall." Journal of Korea Water Resources Association, Vol. 35, No. 5, pp. 575-581. https://doi.org/10.3741/JKWRA.2002.35.5.575
  39. Zhang, J., Qi, Y., Kingsmill, D., and Howard, K. (2012). "Radar-based quantitative precipitation estimation for the cool season in complex terrain: Case studies from the NOAA hydrometeorology testbed." Journal of Hydrometeorology, Vol. 13, No. 6, pp. 1836-1854. https://doi.org/10.1175/JHM-D-11-0145.1