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http://dx.doi.org/10.5351/KJAS.2014.27.3.475

Multi-Site Stochastic Weather Generator for Daily Rainfall in Korea  

Kwak, Minjung (Department of Statistics, Yeungnam University)
Kim, Yongku (Department of Statistics, Kyungpook National University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.3, 2014 , pp. 475-485 More about this Journal
Abstract
A stochastic weather generator based on a generalized linear model (GLM) approach is a commonly used tools to simulate a time series of daily weather. In this paper, we propose a multi-site weather generator with applications to historical data in South Korea. The proposed method extends the approach of Kim et al. (2012) by considering spatial dependence in the model. To reduce this phenomenon, we also incorporate a time series of seasonal mean precipitations of South Korea in the GLM weather generator as a covariate. Spatial dependence was incorporated into the model through a latent Gaussian process. We apply the proposed model to precipitation data provided by 62 stations in Korea from 1973{2011.
Keywords
Daily precipitation; generalized linear model; multisite stochastic weather generator; spatial process; overdispersion;
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  • Reference
1 McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models. 2nd ed., Chapman and Hall.
2 Kim, Y., Katz, R. W., Rajagopalan, B., Furrer, E. M. and Podesta, G. (2012). Reducing overdispersion in stochastic weather generators using a generalized linear modeling approach, Climate Research, 53, 13-24.   DOI
3 Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, N., Rust, H. W., Sauter, T., ThemeBl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M. and Thiele-Eich, I. (2010). Precipitation downscaling under climate change: Recent devel-opments to bridge the gap between dynamical models and the end user, Reviews of Geophysics, 48, doi:10.1029/2009RG000314.   DOI   ScienceOn
4 Stern, R. D. and Coe, R. (1984). A model tting analysis of daily rainfall data, Journal of the Royal Statistical Society A, 147, 1-34.   DOI   ScienceOn
5 Wilks, D. S. (1989). Conditioning stochastic daily precipitation models on total monthly precipitation, Water Resources Research, 25, 1429-1439.   DOI
6 Wilks, D. S. (2010). Use of stochastic weather generators for precipitation downscaling, Wiley Interdisci-plinary Reviews: Climate Change, 1, doi:10.1002/wcc.85   DOI
7 Wilks, D. S. and Wilby, R. L. (1999). The weather generator game: A review of stochastic weather models, Progress in Physical Geography, 23, 329-357.   DOI
8 Hansen, J. W. and Mavromatis, T. (2001). Correcting low-frequency variability bias in stochastic weather generators, Agricultural and Forest Meteorology, 109, 297-310.   DOI   ScienceOn
9 Buishand, T. A. (1978). Some remarks on the use of daily rainfall models, Journal of Hydrology, 47, 235-249.
10 Cleveland, W. S. (1979). Robust locally-weighted regression and smoothing scatterplots, Journal of the American Statistical Association, 74, 829-836.   DOI   ScienceOn
11 Furrer, E. M. and Katz, R. W. (2007). Generalized linear modeling approach to stochastic weather generators, Climate Research, 34, 129-144.   DOI
12 Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models, Chapman and Hall, New York.
13 Katz, R. W. and Parlange, M. B. (1998). Overdispersion phenomenon in stochastic modeling of precipitation, Journal of Climate, 11, 591-601.   DOI
14 Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resources Research, 17, 182-190.   DOI
15 Katz, R. W. and Zheng, X. (1999). Mixture model for overdispersion of precipitation, Journal of Climate, 12, 2528-2537.   DOI