Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Stochastic precipitation modeling based on Korean historical data

  • Received : 2012.10.30
  • Accepted : 2012.11.20
  • Published : 2012.11.30
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Abstract

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

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References

  1. Benestad, R. E., Hanssen-Bauer, I. and Chen, D. (2008). Empirical-statistical downscaling, World Scientific, Singapore.
  2. Buishand, T. A. (1978). Some remarks on the use of daily rainfall models. Journal of Hydrology, 47, 235-249.
  3. Cleveland, W. S. (1979). Robust locally-weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 829-836. https://doi.org/10.1080/01621459.1979.10481038
  4. Furrer, E. M. and Katz, R. W. (2007). Generalized linear modeling approach to stochastic weather generators. Climate Research, 34, 129-144. https://doi.org/10.3354/cr034129
  5. Hansen, J. W. and Mavromatis, T. (2001). Correcting low-frequency variability bias in stochastic weather generators. Agricultural and Forest Meteorology, 109, 297-310. https://doi.org/10.1016/S0168-1923(01)00271-4
  6. Hastie, T. J. and Tibshirani, R. J. (1990). Generalized additive models, Chapman and Hall, New York.
  7. Katz, R. W. and Parlange, M. B. (1998). Overdispersion phenomenon in stochastic modeling of precipitation. Journal of Climate, 11, 591-601. https://doi.org/10.1175/1520-0442(1998)011<0591:OPISMO>2.0.CO;2
  8. Katz, R. W. and Zheng, X. (1999). Mixture model for overdispersion of precipitation. Journal of Climate, 12, 2528-2537. https://doi.org/10.1175/1520-0442(1999)012<2528:MMFOOP>2.0.CO;2
  9. Kim, Y., Katz, R. W., Rajagopalanc, 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. https://doi.org/10.3354/cr01071
  10. Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R.E., Kendon, E. J., Widmann, M., Brienen, N., Rust, H. W., Sauter, T., Theme l, M. et al. (2010). Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48, doi:10.1029/2009RG000314.
  11. McCullagh, P. and Nelder, J. A. (1989). Generalized linear models, 2d ed., Chapman and Hall.
  12. Mehrotra, R., Sharma, A. and Cordery, I. (2004). Comparison of two approaches for downscaling synop- tic atmospheric patterns to multisite precipitation occurrence. Journal of Geophysical Research D: Atmospheres, 109, 1-15.
  13. 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
  14. Stern, R. D. and Coe, R. (1984). A model fitting analysis of daily rainfall data. Journal of the Royal Statistical Society A, 147, 1-34. https://doi.org/10.2307/2981736
  15. 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. https://doi.org/10.1177/030913339902300302
  16. Wilks, D. S. (2010) Use of stochastic weather generators for precipitation downscaling. Wiley Interdisciplinary Reviews: Climate Change, 1, doi:10.1002/wcc.85
  17. Wilks, D. S. (1989). Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resources Research, 25, 1429-1439. https://doi.org/10.1029/WR025i006p01429

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