Generation of radar rainfall data for hydrological and meteorological application (Ⅱ) : radar rainfall ensemble

Tae-Jeong Kim; Dong-Ryul Lee; Sang-Min Kang; Hyun-Han Kwon

doi:10.3741/JKWRA.2017.50.1.17

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Generation of radar rainfall data for hydrological and meteorological application (Ⅱ) : radar rainfall ensemble 수문기상학적 활용을 위한 레이더 강우자료 생산(Ⅱ) : 레이더 강우앙상블

31 January 2017. pp. 17-28

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Abstract

A recent increase in extreme weather events and flash floods associated with the enhanced climate variability results in an increase in climate-related disasters. For these reasons, various studies based on a high resolution weather radar system have been carried out. The weather radar can provide estimates of precipitation in real-time over a wide area, while ground-based rain gauges only provides a point estimate in space. Weather radar is thus capable of identifying changes in rainfall structure as it moves through an ungauged basin. However, the advantage of the weather radar rainfall estimates has been limited by a variety of sources of uncertainty in the radar reflectivity process, including systematic and random errors. In this study, we developed an ensemble radar rainfall estimation scheme using the multivariate copula method. The results presented in this study confirmed that the proposed ensemble technique can effectively reproduce the rainfall statistics such as mean, variance and skewness (more importantly the extremes) as well as the spatio-temporal structure of rainfall fields.

Keywords

Weather Radar

Radar Rainfall

Copula

Rainfall Ensemble

최근 국지성 집중호우 및 돌발홍수와 같은 급격한 기상변화로 인한 기상재해의 발생빈도가 증가함에 따라 고해상도의 기상레이더 강우자료를 사용한 수공학 분야의 연구가 활발하게 진행되고 있다. 기상레이더는 넓은 지역에 걸쳐 실시간으로 강우현상 감시가 가능하며 지상우량계로는 파악이 불가능한 미계측유역을 통과하는 강우장의 이동 및 변동성 파악이 가능한 장점이 있지만 대기 중 존재하는 수상체로부터 반사되는 반사도를 사용하여 강우량을 산정하므로 시공간적 오차가 존재한다. 본 연구에서는 이러한 문제점을 해결하기 위하여 다변량 Copula 함수를 활용하여 레이더 강우에 존재하는 시공간적 오차를 규명하고 레이더 강우앙상블 생산기법을 개발하였다. 개발된 모형으로부터 생산된 레이더 강우앙상블은 통계적 효율기준 분석결과 우수한 모형성능을 확인하였으며 추가적으로 극치호우 및 강우시계열 패턴 분석결과 지상강우의 특성을 효과적으로 재현하는 것을 확인하였다.

키워드

기상레이더

레이더 강우

Copula

강우앙상블

References

AghaKouchak, A., Bardossy, A., and Habib, E. (2010). “Copula-based uncertainty modelling: application to multisensor precipitation estimates.” Hydrological Processes, Vol. 24, No. 15, pp. 2111-2124.

Dai, Q., Han, D., Rico-Ramirez, M. A., and Islam, T. (2013). “Impact of rain drop drift in three-dimensional wind field on radar–gauge rainfall comparison.” International Journal of Remote Sensing, Vol. 34, No. 21, pp. 7739-7760.

Dai, Q., Han, D., Rico-Ramirez, M. A., and Islam, T. (2014). “Modelling radar-rainfall estimation uncertainties using elliptical and Archi-medean copulas with different marginal distributions.” Hydrological Sciences Journal, Vol. 59, No. 11, pp. 1992-2008.

Germann, U., Berenguer, M., Sempere-Torres, D., and Zappa, M. (2009). “Real-ensemble radar precipitation estimation for hydrology in a mountainous region.” Quarterly Journal of the Royal Meteorological Society, Vol. 135, No. 639, pp. 445-456.

Hewitt, C. D., and Griggs, D. J. (2004). “Ensembles-based predictions of climate changes and their impacts (ENSEMBLES).” Eos, Vol. 85, No. 52, p. 566.

Joe, H. (1997). “Multivariate models and dependence concepts.” Chapman Hall, London.

Kim, S. J., Noh, H. S., Kang, N. R., Lee, K. H., Kim, Y. S., Lim, S. H., Lee, D. R., and Kim, H. S. (2014). “Noise reduction analysis of radar rainfall using chaotic dynamics and filtering techniques.” Advances in Meteorology, Vol. 2014.

Kober, K., Craig, G. C., Keil, C., and Dörnbrack, A. (2012). “Blending a probabilistic nowcasting method with a high‐resolution numerical weather prediction ensemble for convective precipitation forecasts.” Quarterly Journal of the Royal Meteorological Society, Vol. 138, No. 664, pp. 755-768.

Krajewski, W. F., and Smith, J. A. (2002). “Radar hydrology: rainfall estimation.” Advances in Water Resources, Vol. 25, No. 8, pp. 1387-1394.

Lee, K. H., Lim, S. H., Jang, B. J., and Lee, D. R. (2015). “Quantitative rainfall estimation for S-band dual polarization radar using distributed specific differential phase.” Journal of Korea Water Resources Association, Vol. 48, No. 1, pp. 57-67 (in Korean).

Melchiori, M. R. (2003). “Which Archimedean copula is the right one?.” Yield Curve, October.

Molteni, F., Buizza, R., Palmer, T. N., and Petroliagis, T. (1996). “The ECMWF ensemble prediction system: methodology and validation.” Quarterly Journal of the Royal Meteorological Society, Vol. 122, No. 529, pp. 73-119.

Priestley, M. B. (1981). “Spectral analysis and time series.”

Salvadori, G., De Michele, C., Kottegoda, N. T., and Rosso, R. (2007). “Extremes in nature: an approach using copulas.” Springer Science & Business Media, Vol. 56.

Schmidt, R. (2002). “Tail dependence for elliptically contoured distributions.” Mathematical Methods of Operations Research, Vol. 55, No. 2, pp. 301-327.

Sklar, M. (1959). “Fonctions de repartition a n dimensions et leurs marges.” Universite Paris 8.

Villarini, G., Serinaldi, F., and Krajewski, W. F. (2008). “Modeling radar-rainfall estimation uncertainties using parametric and non-parametric approaches.” Advances in Water Resources, Vol. 31, No. 12, pp. 1674-1686.

Vogl, S., Laux, P., Qiu, W., Mao, G., and Kunstmann, H. (2012). “Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields.” Hydrology and Earth System Sciences, Vol. 16, No. 7, pp. 2311-2328.

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 50
No :1
Pages :17-28
Received Date : 2016-11-01
Revised Date : 2016-11-22
Accepted Date : 2016-11-22
DOI :https://doi.org/10.3741/JKWRA.2017.50.1.17

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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