Improving the simulation of extreme precipitation events by stochastic weather generators

Water Resources Research

Volumn 44, Issue 12, 2008, Pages

  Furrer, Eva M ^a   Katz, Richard W ^a  

^a NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; CLIMATOLOGY; PROGRAMMING THEORY; RANDOM PROCESSES; WIND EFFECTS;

ALTERNATIVE APPROACHES; CLIMATE VARIABILITIES; EXTREMAL BEHAVIORS; EXTREME EVENTS; EXTREME PRECIPITATIONS; EXTREME VALUE THEORIES; GAMMA DISTRIBUTIONS; GENERALIZED PARETO DISTRIBUTIONS; HIGH INTENSITIES; HYBRID TECHNIQUES; INHERENT LIMITATIONS; PRECIPITATION INTENSITIES; RE SAMPLINGS; REALISTIC MODELING; STATISTICAL TOOLS; STOCHASTIC WEATHER GENERATORS; STRETCHED EXPONENTIAL; TIME-SCALE; WEATHER GENERATORS; WEIBULL;

WEIBULL DISTRIBUTION;

CLIMATE VARIATION; PRECIPITATION INTENSITY; STOCHASTICITY; WEATHER;

EID: 59649107992     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2008WR007316     Document Type: Article

References (32)

1. Abaurrea, J., and A. C. Cebrián (2002), Drought analysis based on a cluster Poisson model: Distribution of the most severe drought, Clim. Res., 22, 227-235.
2. Cameron, D., K. Bevin, and J. Tawn (2001), Modelling extreme rainfalls using a modified pulse Bartlett-Lewis stochastic rainfall model (with uncertainty), Adv. Water Resour., 24, 203-211.
3. Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values, Springer, London.
4. Cook, N. J., and R. Harris (2004), Exact and general FT1 penultimate distributions of extreme wind speeds drawn from tail-equivalent Weibull parents, Struct. Saf., 26, 391-420.
5. Efron, B., and R. J. Tibshirani (1993), An Introduction to the Bootstrap, Chapman and Hall, Boca Raton, Fla.
6. Embrechts, P., C. Klüppelberg, and T. Mikosch (1997), Modelling Extremal Events for Insurance and Finance, Springer-Verlag, Berlin.
7. Furrer, E. M., and R. W. Katz (2007), Generalized linear modeling approach to stochastic weather generators, Clim. Res., 34, 129-144.
8. Johnson, G. L., C. L. Hanson, S. P. Hardegree, and E. B. Ballard (1996), Stochastic weather simulation: Overview and analysis of two commonly used models, J. Appl. Meteorol., 35, 1878-1896.
9. Katz, R. W. (1996), Use of conditional stochastic models to generate climate change scenarios, Clim. Change., 32(3), 237-255.
10. Katz, R. W., M. B. Parlange, and P. Naveau (2002), Statistics of extremes in hydrology, Adv. Water Resour., 25, 1287-1304.
11. Koutsoyiannis, D. (2004), Statistics of extremes and estimation of extreme rainfall. 2: Empirical investigation of long rainfall records, Hydrol. Sci. J., 49, 591-610.
12. Leadbetter, M. R., G. Lindgren, and H. Rootzén (1983), Extremes and Related Properties of Random Sequences and Processes, Springer-Verlag, New York.
13. Madsen, H., P. Rasmussen, and D. Rosbjerg (1997), Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events: 1. At-site modeling, Water Resour. Res., 33, 747-757.
14. McCullagh, P., and J. A. Nelder (1989), Generalized Linear Models, 2nd ed., Chapman and Hall, London.
15. Qian, B., S. Gameda, and H. Hayhoe (2008), Performance of stochastic weather generators LARS-WG and AAFC-WG for reproducing daily extremes of diverse Canadian climates, Clim. Res., 37, 17-33.
16. Rajagopalan, B., and U. Lall (1999), A k-nearest-neighbor simulator for daily precipitation and other weather variables, Water Resour. Res., 35, 3089-3101.
17. Reiss, R.-D., and M. Thomas (2007), Statistical Analysis of Extreme Values with Applications to Insurance, Finance, Hydrology and Other Fields, 3rd ed., Birkhäuser, Basel.
18. Richardson, C. W. (1981), Stochastic simulation of daily precipitation, temperature and solar radiation, Water Resour. Res., 17, 182-190.
19. Schwarz, G. (1978), Estimating the dimension of a model, Ann. Stat., 6, 461-464.
20. Semenov, M. A. (2008), Simulation of extreme weather events by a stochastic weather generator, Clim. Res., 35, 203-212.
21. Sharif, M., and D. H. Burn (2006), Simulating climate change scenarios using an improved k-nearest neighbor model, J. Hydrol., 325, 179-196.
22. Smith, R. L. (1989), Extreme value analysis of environmental time series: An example based on ozone data (with discussion), Stat. Sci., 4, 361-393.
23. Smith, R. E., and H. A. Schreiber (1974), Point process of seasonal thunderstorm rainfall: 2. Rainfall depth probabilities, Water Resour. Res., 10, 418-423.
24. Vrac, M., and P. Naveau (2007), Stochastic downscaling of precipitation: From dry events to heavy rainfalls, Water Resour. Res., 43, W07402, doi:10.1029/2006WR005308.
25. Wilby, R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks (1998), Statistical downscaling of general circulation model output: A comparison of methods, Water Resour. Res., 34, 2995-3008.
26. Wilks, D. S. (1989), Rainfall intensity, the Weibull distribution, and estimation of daily surface runoff, J. Appl. Meteorol., 28, 52-58.
27. Wilks, D. S. (1992), Adapting stochastic weather generation algorithms for climate change studies, Clim. Change, 22, 67-84.
28. Wilks, D. S. (1999), Interannual variability and extreme-value characteristics of several stochastic daily precipitation models, Agric. For. Meteorol., 93, 153-169.
29. Wilks, D. S., and R. L. Wilby (1999), The weather generation game: A review of stochastic weather models, Prog. Phys. Geogr., 23(3), 329-357.
30. Wilson, P. S., and R. Toumi (2005), A fundamental probability distribution for heavy rainfall, Geophys. Res. Lett., 32, L14812, doi:10.1029/2005GL022465.
31. Wong, R. K. W. (1977), Weibull distribution, iterative likelihood technique and hydrometeorological data, J. Appl. Meteorol., 16, 1360-1364.
32. Yan, Z., S. Bate, R. E. Chandler, V. Isham, and H. Wheater (2006), Changes in extreme wind speeds in NW Europe simulated by generalized linear models, Theor. Appl. Climatol., 83, 121-137.