SEARCH

SEARCH BY CITATION

References

  • Bárdossy, A., and E. Plate (1992), Space-time model for daily rainfall using atmospheric circulation patterns, Water Resour. Res., 28, 12471259.
  • Barnston, A. G., and R. E. Livezey (1987), Classification, seasonality and persistence of low-frequency atmospheric circulation patterns, Mon. Weather Rev., 115, 10831126.
  • Buishand, T., and T. Brandsma (2001), Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbor resampling, Water Resour. Res., 37, 27612776.
  • Chandler, R. (2002), GLIMCLIM: Generalized linear modelling for daily climate time series (software and user guide), Tech. Rep. 227, Dep. of Stat. Sci., Univ. College London, London.
  • Chandler, R. (2005), On the use of generalized linear models for interpreting climate variability, Environmetrics, 16(7), 699715.
  • Chandler, R., and H. S. Wheater (1998), Climate change detection using generalized linear models for rainfall—A case study from the west of Ireland. II. Modelling of rainfall amounts on wet days, Tech. Rep. 195, Dep. of Stat. Sci., Univ. College London, London.
  • Chandler, R., and H. S. Wheater (2002), Analysis of rainfall variability using generalized linear models: A case study from the west of Ireland, Water Resour. Res., 38(10), 1192, doi:10.1029/2001WR000906.
  • Charles, S., B. Bates, and J. Hughes (1999), A spatiotemporal model for downscaling precipitation occurrence and amounts, J. Geophys. Res., 104(D24), 31,65731,669.
  • Coe, R., and R. D. Stern (1982), Fitting models to daily rainfall, J. Appl. Meteorol., 21, 10241031.
  • Coles, S. (2001), An Introduction to the Statistical Modelling of Extreme Values, Springer, New York.
  • Cox, D., and N. Wermuth (1996), Multivariate Dependencies: Models, Analysis and Interpretation, CRC Press, Boca Raton, Fla.
  • Cressie, N. (1991), Statistics for Spatial Data, John Wiley, Hoboken, N. J.,
  • Dobson, A. (2001), An Introduction to Generalized Linear Models, 2nd ed., CRC Press, Boca Raton, Fla.
  • Embrechts, P., C. Klüppelberg, and T. Mikosch (1997), Modelling Extremal Events for Insurance and Finance, Springer, New York.
  • Emrich, L., and M. Piedmonte (1991), A method for generating high-dimensional multivariate binary variates, Am. Stat., 45, 302304.
  • Hughes, J. P., P. Guttorp, and S. Charles (1999), A nonhomogeneous hidden Markov model for precipitation, Appl. Stat., 48, 1530.
  • Hurrell, J. W. (1995), Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation, Science, 269, 676679.
  • Institute of Hydrology (1999), Flood Estimation Handbook, 5, vols., Wallingford, U. K.
  • Jones, P. D., T. Jónsson, and D. Wheeler (1997), Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and south-west Iceland, Int. J. Climatol., 17, 14331450.
  • Katz, R., M. Parlange, and P. Naveau (2002), Statistics of extremes in hydrology, Adv. Water Resour., 25, 12871304.
  • Krzanowski, W. (1988), Principles of Multivariate Analysis, Oxford Univ. Press., New York.
  • Lunn, A., and S. Davies (1998), A note on generating correlated binary variables, Biometrika, 85, 487490.
  • McCullagh, P., and J. Nelder (1989), Generalized Linear Models, 2nd ed., CRC Press, Boca Raton, Fla.
  • Murphy, S. J., and R. Washington (2001), United Kingdom and Ireland precipitation variability and the North Atlantic sea-level pressure field, Int. J. Climatol., 21, 939959.
  • Oman, S., and D. Zucker (2001), Modelling and generating correlated binary variables, Biometrika, 88, 287290.
  • Ryan, L. (1995), Comment on the article by Liang and Zeger, Stat. Sci., 10, 189193.
  • Stehlík, J., and A. Bárdossy (2002), Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation, J. Hydrol., 256, 120141.
  • Stern, R. D., and R. Coe (1984), A model fitting analysis of rainfall data (with discussion), J. R. Stat. Soc., Ser. A, 147, 134.
  • Terrell, G. (2003), The Wilson-Hilferty transformation is locally saddlepoint, Biometrika, 90, 445453.
  • Wheater, H. (2002), Progress in and prospects for fluvial flood modelling, Proc. R. Soc. Lond., Ser. A, 360, 14091432.
  • Wheater, H. S., V. S. Isham, C. Onof, R. E. Chandler, P. J. Northrop, P. Guiblin, S. M. Bate, D. R. Cox, and D. Koutsoyiannis (2000), Generation of spatially consistent rainfall data, Tech. Rep. 204, Dep. of Stat. Sci., Univ. Coll. London, London.
  • Wilks, D. (1998), Multisite generalization of a daily stochastic precipitation generation model, J. Hydrol., 210, 178191.
  • Wilks, D., and R. Wilby (1999), The weather generation game: A review of stochastic weather models, Prog. Phys. Geogr., 23, 329357.
  • Williams, D. (1982), Extra-binomial variation in logistic linear models, Appl. Stat., 31, 144148.
  • Yan, Z., S. Bate, R. Chandler, V. Isham, and H. Wheater (2002), An analysis of daily maximum windspeed in northwestern Europe using generalized linear models, J. Clim., 15, 20732088.
  • Yan, Z., S. Bate, R. Chandler, V. Isham, and H. Wheater (2005), Changes in extreme wind speeds in NW Europe simulated by generalized linear models, Theor. Appl. Climatol., doi:10.1007/s00704-005-0156-x, in press.
  • Yang, C. (2001), Observed changes and simulative predictions of climate extremes in China (in Chinese), Ph.D. thesis, Inst. of Atmos. Phys., Beijing.
  • Yang, C., R. Chandler, V. Isham, C. Annoni, and H. Wheater (2005), Simulation and downscaling models for potential evaporation, J. Hydrol., 302, 239254.