The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Threshold Modelling of Spatial Extremes - Summer Rainfall of Korea

공간 극단값의 분계점 모형 사례 연구 - 한국 여름철 강수량

  • Hwang, Seungyong (Department of Statistics, Chonbuk National University) ;
  • Choi, Hyemi (Department of Statistics, Chonbuk National University)
  • Received : 2014.06.11
  • Accepted : 2014.08.06
  • Published : 2014.08.31
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Abstract

An adequate understanding and response to natural hazards such as heat wave, heavy rainfall and severe drought is required. We apply extreme value theory to analyze these abnormal weather phenomena. It is common for extremes in climatic data to be nonstationary in space and time. In this paper, we analyze summer rainfall data in South Korea using exceedance values over thresholds estimated by quantile regression with location information and time as covariates. We group weather stations in South Korea into 5 clusters and t extreme value models to threshold exceedances for each cluster under the assumption of independence in space and time as well as estimates of uncertainty for spatial dependence as proposed in Northrop and Jonathan (2011).

폭염, 폭우와 가뭄 등과 같은 이상 기후 현상에 대한 적절한 대응이 최근 많이 요구되고 있다. 이상 기후 현상을 분석하기 위해 극단값 분석 기법을 적용할 수 있는데, 본 논문은에서는 한국의 여름철 강수량 자료(1973년부터 2012년까지의 5월부터 9월)를 분계점 초과값 모형으로 분석해보았다. 분계점은 한국의 기상관측소들을 5개의 군집으로 나누어, 각 군집별로 지리 정보와 시간을 공변량으로 하는 분위수 회귀 방법을 통하여 추정하였다. Northrop과 Jonathan (2011)과 같이 극단값들이 시공간적으로 독립이라고 가정하고 분석한 후, 추정오차와 검정 과정에 공간 종속성을 반영하였다.

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References

  1. Chandler, R. E. and Bate, S. B. (2007). Inference for clustered data using the independence log-likelihood, Biometrika, 94, 167-183. https://doi.org/10.1093/biomet/asm015
  2. Choi, Y. E. (2004). Trends on temperature and precipitation extreme events in Korea, Journal of the Korean Geographical Society, 39, 711-721.
  3. Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, London.
  4. Davison, A. C., Padoan, S. A. and Ribatet, M. (2012). Statistical modeling of spatial extremes, Statistical Science, 27, 161-186. https://doi.org/10.1214/11-STS376
  5. Davison, A. C. and Smith, R. L. (1990). Models for exceedances over high thresholds (with discussion), Journal of the Royal Statistical Society Series B, 52, 393-442.
  6. Jung, J. Y., Jin, S. H. and Park, M. S. (2008). Precipitation analysis based on spatial linear regression model, The Korean Journal of Applied Statistics, 21, 1096-1107. https://doi.org/10.5351/KJAS.2008.21.6.1093
  7. Kim, S. Y., Kwon, Y. A., Park, C. Y. and Chun, J. H. (2008). Regional characteristics of the monthly variability of precipitation from May to September over South Korea, Journal of Climate Research, 2, 64-75.
  8. Koenker, R. and Bassett, G. Jr. (1978). Regression quantiles, Econometrica, 46, 33-50. https://doi.org/10.2307/1913643
  9. Lee, K. M., Baek, H. J. and Cho, C. H. (2012). Analysis of changes in extreme precipitation in Seoul using quantile regression, Journal of Climate Research, 3, 199-209.
  10. Lee, S. H. and Kwon, W. T. (2004). A variation of summer rainfall in Korea, Korean Geographical Society, 39, 819-832.
  11. Northrop, P. J. and Jonathan, P. (2011). Threshold modelling of spatially dependent non-stationary extremes, Environmetrics, 22, 799-809. https://doi.org/10.1002/env.1106
  12. Park, M. S. and Kim, H. Y. (2008). Classification of precipitation data based on smoothed periodogram, the Korean Journal of Applied Statistics, 21, 547-560. https://doi.org/10.5351/KJAS.2008.21.3.547
  13. Smith R. L. (1989). Extreme value analysis of environmental time series : An application to trend detection in ground-level ozone, Statistical Science, 4, 367-377. https://doi.org/10.1214/ss/1177012400
  14. 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, 117-133. https://doi.org/10.12652/Ksce.2014.34.1.0117