Browse > Article
http://dx.doi.org/10.7465/jkdi.2014.25.1.245

Stochastic simulation of daily precipitation: A copula approach  

Choi, Changhui (Korea Insurance Research Institute)
Ko, Bangwon (Department of Statistics and Actuarial Science, Soongsil University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.1, 2014 , pp. 245-254 More about this Journal
Abstract
The traditional methods of simulating daily precipitation have paid little attention to the inherent dependence structure between the total precipitation amount and the precipitation frequency for a fixed period of time. To address this issue, we propose a new simulation algorithm using copula in order to incorporate the dependence into the traditional methods. The algorithm consists of two parts: First, while reflecting the observed dependence, we generate the total precipitation amount (S) and the frequency (N) during the period of interest; then we simulate the daily precipitation whose aggregation matches the pair of (N; S) generated in the first part. Our result shows that the proposed method substantially improves the traditional methods.
Keywords
Copula; daily precipitation simulation; gamma distribution; Markov chain;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Cherubini, U., Luciano, E. and Vecchiato, W. (2004). Copula methods in finance, John Wiley & Sons, New Jersey.
2 Chin, E. H. (1977). Modeling daily precipitation occurrence process with Markov chain. Water Resources Research, 13, 949-956.   DOI
3 Choi. C, Lee, H. and Joo, H. (2013), Analyzing rainfall patterns and pricing rainfall insurance using copula. Journal of the Korean Data & Information Science Society, 24, 1598-9402.   과학기술학회마을   DOI   ScienceOn
4 Duan, J., Selker, J. and Gordon, E. G. (1998). Evaluation of probability density function in precipitation models for the Pacic Northwest. Journal of the American Water Resources Association, 24, 617-627.
5 Favre, A.-C., El Adouni, S., Perreault, L., Thiemonge, N. and Bobee, B. (2004). Multivariate hydrological frequency analysis using copulas. Water Resources Research, 40 .
6 Genest, C. and Favre, A.-C. (2007). Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 12, 347-368.   DOI   ScienceOn
7 Pickering, N. B., Stedinger, J. R. and Haith, D. A. (1988). Weather input for nonpoint-source pollution models. Journal of Irrigation and Drainage Engineering, 114, 674-690.   DOI
8 Richardson C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Research, 17, 182-190.   DOI
9 Richardson, C. W. and Wright, D. A. (1984). WGEN: A model for generating daily weather variables. Agricultural Research Service, ARS-8, 83, Washington DC, USA.
10 Ross, S. M. (2007). Introduction to probability models, 9th Edition, Academic Press, New York.
11 Sharpley, A. N. and Williams, J. R. (1990a). Erosion/productivity impact calculator 1. Model documentation , Technical Bulletin 1768, U. S. Department of Agriculture, Washington DC, USA.
12 Sharpley, A. N. and Williams, J. R. (1990b). Erosion/productivity impact calculator 2. User manual, Technical Bulletin 1768, U.S. Department of Agriculture, Washington DC, USA.
13 Sklar, A. (1959). Fonctions de repartition a n dimensions et leurs marges. Paris Institute of Statistics, 8, 229-231.
14 Stern, R. D. (1980). The calculation of probability distributions for models of daily precipitation. Archiv fur Meteorologie, Geophysik und Bioklimatologie B, 28, 137-137.   DOI
15 Stern, R. D. and Coe, R. (1984). A model fitting analysis of daily rainfall data. Journals of the Royal Statistical Society A, 147, 1-34.   DOI   ScienceOn
16 Wan, H., Zhang, X. and Barrow, E. M. (2005). Stochastic modeling of daily precipitation for Canada. Atmosphere-Ocean, 43, 23-32.   DOI   ScienceOn
17 Katz, R. W. and Parlange, M. B. (1998) Over dispersion phenomenon in stochastic modeling of precipitation. Journal of Climate, 11, 591-601.   DOI
18 Hayhoe, H. N. (2000). Improvements of stochastic weather data generators for diverse climates. Climate Research, 14, 75-87.   DOI
19 Hogg, R. V. and Craig, A. T. (1978). Introduction to mathematical statistics, 4th Edition, Macmillan, New York.
20 Joe, H. (1997) Multivariate models and dependence concepts, Chapman and Hall, New York.
21 Kim, E. and Lee, T. (2011). A numerical study on portfolio VaR forecasting based on conditional copula, Journal of the Korean Data & Information Science Society, 22, 1065-1074.   과학기술학회마을
22 Kittel, T. G. F., Rosenbloom, N. A., Painter, T. H., Schimel, D. S. and VEMAP Modeling Participants. (1995). The VEMAP integrated database for modeling United States ecosystem/vegetation sensitivity to climate change. Journal of Biogeography, 22, 857-862.   DOI   ScienceOn
23 Leobacher, G. and Ngare, P. (2011). On modeling and pricing rainfall derivatives with seasonality. Applied Mathematical Finance, 18, 71-91.   DOI   ScienceOn
24 Malevergne, Y. and Sornette, D. (2006). Extreme financial risks: From dependence to risk management, Springer, New York.
25 Nelsen, R. B. (2006). An introduction to copulas, Springer, New York.
26 Nicks, A. D. and Gander, G. A. (1994). CLIGEN: A weather generator for climate inputs to water resource and other models. In Proceedings Fifth International Conference on Computers in Agriculture, American Society of Agricultural Engineers, Orlando, FL, 903-909.
27 Zhang, L. and Singh, V. P. (2007). Bivariate rainfall frequency distributions using Archimedean copulas. Journal of Hydrology, 332, 93-109.   DOI   ScienceOn
28 Wilks, D. S. (1998). Multisite generalization of a daily stochastic precipitation generation model. Journal of Hydrology, 210, 178-191.   DOI   ScienceOn