A stochastic daily weather generator for skewed data

Water Resources Research

Volumn 46, Issue 7, 2010, Pages

  Flecher, C ^a,b   Naveau, P ^a   Allard, D ^c   Brisson, N ^b  

^a LABORATOIRE DES SCIENCES DU CLIMAT ET DE L ENVIRONNEMENT   (France)

^b French National Institute for Agricultural Research   (France)

^c INRA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS OF DISTRIBUTIONS; CLUSTERING TECHNIQUES; FIRST-ORDER; GLOBAL RADIATION; MARKOV CHAIN; MAXIMUM TEMPERATURE; NONSYMMETRIC; PRECIPITATION INTENSITY; SKEW-NORMAL DISTRIBUTION; SKEWED DATA; STATISTICAL PROPERTIES; WEATHER GENERATOR; WET AND DRY; WIND DISTRIBUTION; WIND SPEED;

ATMOSPHERIC RADIATION; MARKOV PROCESSES; TIME SERIES;

NORMAL DISTRIBUTION;

MARKOV CHAIN; MULTIVARIATE ANALYSIS; PRECIPITATION INTENSITY; SKEWNESS; SOLAR RADIATION; STOCHASTICITY; TEMPERATURE EFFECT; TIME SERIES; WEATHER FORECASTING; WIND POWER; WIND VELOCITY;

FRANCE;

EID: 77955196380     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009WR008098     Document Type: Article

References (30)

1. Allard, D., and P. Naveau (2007) A new spatial skew-normal random field model, Commun. Stat., 36, 1821-1834.
2. Apipattanavis, S. G., G. Podesta, B. Rajagopalan, and R. W. Katz (2007), A semiparametric multivariate and multisite weather generator, Water Resour. Res., 43, W11401, doi:10.1029/2006WR005714.
3. Azzalini, A., and A. Capitanio (1999), Statistical applications of the multivariate skew-normal distribution, J. R. Stat. Soc., Ser. B, 61, 579-602.
4. Boé, J., and L. Terray (2008), A weather type approach to analysing winter precipitation in France: Twentieth century trends and influence of anthropogenic forcing, J. Clim., 21, 3118-3133.
5. Boé, J., L. Terray, F. Habets, and E. Martin (2006), A simple statisticaldynamical downscaling scheme based on weather types and conditional resampling, J. Geophys. Res., 111, D23106, doi:10.1029/2005JD006889. (Pubitemid 47260816)
6. Brisson, N., et al. (2003) An overview of the crop model STICS, Eur. J. Agron., 18, 309-332.
7. Brisson, N., M. Launay, B. Mary, and N. Beaudoin (Eds.) (2009), Conceptual Basis, Formalisations and Parameterization of the STICS Crop Model, 304 pp., Ed. Quae, Versailles, France.
8. Bubnova, R., G. Hello, P. Bernard, and J. F. Geleyn (1995), Integration of the fully elastic equations cast in the hydrostatic pressure terrain following coordinate in the framework of the ARPEGE/Aladin NWP system, Mon. Weather Rev., 123, 515-535.
9. Flecher, C., P. Naveau, and D. Allard (2009), Estimating the closed skewnormal distributions parameters using weighted moments, Stat. Probab. Lett., 79, 1977-1984.
10. Fraley, C., and A. E. Raftery (2003), Enhanced model?based clustering, density estimation, and discriminant analysis software: MCLust, J. Classif., 20, 263-286.
11. Fraley, C., and A. E. Raftery (2006), MCLust version 3 for R: Normal mixture modeling and model?based clustering, Tech. Rep. 504, Dep. of Stat., Univ. of Wash., Seattle.
12. Furrer, E. M., and R. W. Katz (2007), Generalized linear modeling approach to stochastic weather generators, Clim. Res., 34, 129-144.
13. Genton, M. G. (Eds.) (2004), Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, Chapman and Hall, Boca Raton, Fla.
14. Gibelin, A.-L., and M. Déqué (2003) Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model, Clim. Dyn., 20, 327-339.
15. Green, P. J., and B. W. Silverman (1994), Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach, Chapman and Hall, Boca Raton, Fla.
16. González?Farías, G., J. Domínguez?Molina, and A. Gupta (2004), Additive properties of skew-normal random vectors, J. Stat. Plann. Inference, 126, 521-534.
17. Katz, R. W. (1996), The use of stochastic models to generate climate scenarios, Clim. Change, 32, 237-255.
18. Pewsey, A., and G. González?Farías (2007), Skew-elliptical distributions and their application-Preface, Commun. Stat. Theory Methods, 36, 1657-1659.
19. Qian, B., H. Hayhoe, and S. Gameda (2005), Evaluation of the stochastic weather generators LARS?WG and AAFC?WG for climate change impact studies, Clim. Res., 29, 3-21.
20. Rajagopalan, B., and U. Lall (1999), A k?nearest?neighbor simulator for daily precipitation and other weather variables, Water Resour. Res., 35, 3089-3101.
21. Rajagopalan, B., U. Lall, D. G. Tarboton, and D. S. Bowles (1997), Multivariate non parametric resampling sheme for generation of daily weather variables, Stochastic Hydrol. Hydraul., 11(1), 523-547.
22. Richardson, C. W. (1981), Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resour. Res., 17, 182-190.
23. Richardson, C. W., and D. A. Wright (1984), A model for generating daily weather variables, Publ. ARS?8, 83 pp., Agric. Res. Serv., U.S. Dep. of Agric., Washington, D. C.
24. Schwarz, G. (1978), Estimating the dimension of a model, Ann. Stat., 6(2), 461-464.
25. Semenov, M. A., and E. M. Barrow (1997), Use of stochastic weather generator in the development of climate change senarios, Clim. Change, 35, 397-414.
26. Semenov, A. M., R. J. Brooks, E. M. Barrow, and C. W. Richardson (1998), Comparison of the WGEN and LARS?WG stochastic weather generators for diverse climates, Clim. Res., 10, 95-107.
27. Stockle, C. O., P. Steduto, and D. A. Wright (1998), Estimating daily and daytime mean VPD from daily maximum VPD, paper presented at 5th Congress, Eur. Soc. Agron., Nitra, Slovakia.
28. 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. (Pubitemid 350194457)
29. Wilks, D. S. (1997), Forecast values: Prescriptive decision studies, in Economic Values of Weather and Climate Forecasts, edited by R. W. Katz and A. H. Murphy, pp. 109-145, Cambridge Univ. Press, New York.
30. Young, K. C. (1994), A multivariate chain model for simulating climatic parameters from daily data, J. Appl Meteorol., 33, 661-671.