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http://dx.doi.org/10.5351/KJAS.2012.25.2.351

Future Weather Generation with Spatio-Temporal Correlation for the Four Major River Basins in South Korea  

Lee, Dong-Hwan (Department of Statistics, Seoul National University)
Lee, Jae-Yong (Department of Statistics, Seoul National University)
Oh, Hee-Seok (Department of Statistics, Seoul National University)
Lee, Young-Jo (Department of Statistics, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.2, 2012 , pp. 351-362 More about this Journal
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.
Keywords
Weather generator; Hierarchical generalized linear models; spatio-temporal correlation;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 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.   DOI
2 Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models (with discussion), Journal of the Royal Statistical Society B, 58, 619-678.
3 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.   DOI   ScienceOn
4 Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models, Journal of the Royal Statistical Society A, 135, 370-384.   DOI   ScienceOn
5 Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resources Research, 17, 182-190.   DOI
6 Richardson, C. W. and Wright, D. A. (1984). WGEN: A model for generating daily weather variables, US Department of Agriculture, (ARS-8).
7 Wilks, D. S. (1998). Multisite generalizations of a daily stochastic precipitation generation model, Journal of Hydrology, 210, 178-191.   DOI   ScienceOn
8 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.   DOI
9 강문성, 박승우, 진영민 (1998). 기상자료 미계측 지역의 추계학적 기상발생모델, <한국농공학회지>, 40, 57-67.
10 Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics, Applied Statistics, 47, 299-350.
11 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.   DOI
12 Furrer, E. M. and Katz, R. W. (2007). Generalized linear modeling approach to stochastic weather generators, Progress in Physical Geography, 23, 329-357.
13 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.   DOI   ScienceOn