The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Future Weather Generation with Spatio-Temporal Correlation for the Four Major River Basins in South Korea

시공간 상관성을 고려한 일기산출기 모형을 이용한 4대강 유역별 미래 일기 변수 산출

  • Received : 2012.12.12
  • Accepted : 2012.01.31
  • Published : 2012.04.30
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Abstract

Weather generators are statistical tools to produce synthetic sequences of daily weather variables. We propose the multisite weather generators with a spatio-temporal correlation based on hierarchical generalized linear models. We develop a computational algorithm to produce future weather variables that use three different types of green-house gases scenarios. We apply the proposed method to a daily time series of precipitation and average temperature for South Korea.

일기 산출기 모형은 가상의 일기 자료를 생성하는 통계 모형이다. 본 연구는 시공간 상관성이 고려된 다중지점에서의 일기산출 모형을 제안하고, 온실가스 배출 미래 시나리오에 따라 강수량과 평균 기온 일기산출이 가능한 알고리즘을 개발하였다. 제안된 알고리즘은 다단계 일반화 선형모형 하에서 필요한 모수들을 추정하고, 적합된 모형 하에서 일기변수들을 랜덤하게 산출하는 절차이다. 과거 30년간 관측된 우리나라 4대강 유역의 일 강수량 자료와 평균 기온 자료를 가지고 모형을 적합하고, 미래 일별 일기자료 산출에 적용하였다.

Keywords

References

  1. 강문성, 박승우, 진영민 (1998). 기상자료 미계측 지역의 추계학적 기상발생모델, <한국농공학회지>, 40, 57-67.
  2. Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics, Applied Statistics, 47, 299-350.
  3. Furrer, E. M. and Katz, R. W. (2007). Generalized linear modeling approach to stochastic weather generators, Progress in Physical Geography, 23, 329-357.
  4. Jang, M. J., Lee, Y., Lawson, A. B. and Browne, W. J. (2007). A comparison of the hierarchical likelihood and Bayesian approaches to spatial epidemiological modelling, Environmetrics, 18, 809-821. https://doi.org/10.1002/env.877
  5. Kim, T., Ahn, H., Chung, G. and Yoo, C. (2008). Stochastic multi-site generation of daily rainfall occurrence in south Florida, Stochastic Environmental Research and Risk Assessment, 22, 705-717. https://doi.org/10.1007/s00477-007-0180-8
  6. Lee, D., An, H., Lee, Y., Lee, J., Lee, H-S. and Oh, H-S. (2010). Improved multisite stochastic weather generation with applications to historical data in South Korea, Asia-Pacific Journal of Atmospheric Sciences, 46, 497-504. https://doi.org/10.1007/s13143-010-0031-2
  7. Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models (with discussion), Journal of the Royal Statistical Society B, 58, 619-678.
  8. Lee, Y. and Nelder, J. A. (2001). Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions, Biometrika, 88, 987-1006. https://doi.org/10.1093/biomet/88.4.987
  9. Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models, Journal of the Royal Statistical Society A, 135, 370-384. https://doi.org/10.2307/2344614
  10. Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resources Research, 17, 182-190. https://doi.org/10.1029/WR017i001p00182
  11. Richardson, C. W. and Wright, D. A. (1984). WGEN: A model for generating daily weather variables, US Department of Agriculture, (ARS-8).
  12. Wilks, D. S. (1998). Multisite generalizations of a daily stochastic precipitation generation model, Journal of Hydrology, 210, 178-191. https://doi.org/10.1016/S0022-1694(98)00186-3
  13. Wilks, D. S. and Wilby, R. L. (1999). The weather generation game: A review of stochastic weather models, Progress in Physical Geography, 23, 329-357. https://doi.org/10.1177/030913339902300302