Stochastic downscaling of precipitation with neural network conditional mixture models

Water Resources Research

Volumn 47, Issue 10, 2011, Pages

  Carreau, Julie ^a   Vrac, Mathieu ^b  

^a UNIVERSITÉ DE MONTPELLIER   (France)

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

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIONAL FLEXIBILITIES; APPROXIMATION CAPABILITIES; ATMOSPHERIC RESEARCH; BENCHMARK MODELS; CONDITIONAL DENSITY; CONDITIONAL DISTRIBUTION; DOWN-SCALING; GAMMA DISTRIBUTION; GAUSSIAN COMPONENTS; GAUSSIANS; GRID CELLS; LOCAL VARIABLES; LOG LIKELIHOOD; LOG-NORMAL; MEDITERRANEAN REGION; MIXTURE MODEL; NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTIONS; NCEP/NCAR; NEURAL NETWORK MODEL; PRECIPITATION DATA; PREDICTOR VARIABLES; SEA-LEVEL PRESSURE FIELDS; STOCHASTIC WEATHER GENERATOR;

CLIMATOLOGY; MIXTURES; NEURAL NETWORKS; PARETO PRINCIPLE; RAIN; SEA LEVEL; STOCHASTIC SYSTEMS;

STOCHASTIC MODELS;

ARTIFICIAL NEURAL NETWORK; DATA SET; DOWNSCALING; GAUSSIAN METHOD; NUMERICAL MODEL; PRECIPITATION (CLIMATOLOGY); RAINFALL; STOCHASTICITY;

MEDITERRANEAN REGION;

EID: 80053585279     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2010WR010128     Document Type: Article

References (62)

1. Bellone, E., J. P. Hughes, and P. Guttorp (2000), A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts, Clim. Res., 15, 1-12. (Pubitemid 32961857)
2. Bishop, C. M. (1994), Mixture density networks, technical report, Aston Univ., Birmingham, U. K.
3. Bishop, C. M. (1995), Neural Networks for Pattern Recognition, Clarendon, Oxford, U. K.
4. Busuioc, A., R. Tomozeiu, and C. Cacciamani (2008), Statistical downscaling model based on canonical correlation analysis for winter extreme precipitation events in the Emilia-Romania region, Int. J. Climatol., 28, 449-464. (Pubitemid 351454600)
5. Cannon, A. J. (2008), Probabilistic multisite precipitation downscaling by an expanded Bernouilli-Gamma density network, J. Hydrometeorol., 9, 1284-1300.
6. Cannon, A. J. (2011), Quantile regression neural networks: Implementation in R and application to precipitation downscaling, Comput. Geosci., 37(9), 1277-1284.
7. Cannon, A. J., and P. H. Whitfield (2002), Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models, J. Hydrol., 259, 136-151. (Pubitemid 34175769)
8. Carreau, J., and Y. Bengio (2009a), A hybrid Pareto model for asymmetric fat-tailed data: The univariate case, Extremes, 12, 53-76.
9. Carreau, J., and Y. Bengio (2009b), A hybrid Pareto mixture for conditional asymmetric fat-tailed distributions, IEEE Trans. Neural Networks, 20(7), 1087-1101.
10. Carreau, J., P. Naveau, and E. Sauquet (2009), A statistical rainfall-runoff mixture model with heavy-tailed components, Water Resour. Res., 45, W10437, doi:10.1029/2009WR007880.
11. Cawley, G. C., G. J. Janacek, M. R. Haylock, and S. R. Dorling (2007), Predictive uncertainty in environmental modelling, Neural Networks, 20, 537-549. (Pubitemid 46874855)
12. Cho, H.-K., K. P. Bowman, and G. R. North (2004), A comparison of Gamma and lognormal distributions for characterizing satellite rain rates from the tropical rainfall measuring mission, J. Appl. Meteorol., 43, 1586-1597. (Pubitemid 40077815)
13. Coles, S. G., and M. J. Dixon (1999), Likelihood-based inference for extreme value models, Extremes, 2, 5-23.
14. Delrieu, G., et al. (2005), The catastrophic flash-flood event of 8-9 September 2002 in the Gard region, France: A first case study for the Cévennes Vivarais Mediterranean hydrometeorological observatory, J. Hydrometeorol., 6(1), 34-52. (Pubitemid 40367920)
15. Embrechts, P., C. Kluppelberg, and T. Mikosch (1997), Modelling Extremal Events, Springer, New York.
16. Friederichs, P. (2010), Statistical downscaling of extreme precipitation events using extreme value theory, Extremes, 13, 109-132.
17. Friederichs, P., and A. Hense (2007), Statistical downscaling of extreme precipitation events using censored quantile regression, Mon. Weather Rev., 135, 2365-2378. (Pubitemid 47050428)
18. Frigessi, A., O. Haug, and H. Rue (2002), A dynamic mixture model for unsupervised tail estimation without threshold selection, Extremes, 5, 219-235.
19. Gardes, L., and S. Girard (2010), Conditional extremes from heavy-tailed distributions: An application to the estimation of extreme rainfall return levels, Extremes, 13, 177-204.
20. Ghosh, S., and P. P. Mujumdar (2008), Statistical downscaling of GCM simulations to streamflow using relevance vector machine, Adv. Water Resour., 31(1), 132-146. (Pubitemid 350082680)
21. Gladstone, R., et al. (2005), Mid-Holocene NAO: A PMIP2 model intercomparison, Geophys. Res. Lett., 32, L16707, doi:10.1029/2005GL023596. (Pubitemid 41461669)
22. González-Rouco, J. F., H. Heyen, E. Zorita, and F. Valero (2000), Agreement between observed rainfall trends and climate change simulations in the southwest of Europe, J. Clim., 13, 3057-3065.
23. Goubanova, K., V. Echevin, B. Dewitte, F. Codron, K. Takahashi, P. Terray, and M. Vrac (2010), Statistical downscaling of sea-surface wind over the Peru-Chile upwelling region: Diagnosing the impact of climate change from the IPSL-CM4 model, Clim. Dyn., 36(7-8), 1365-1378.
24. Haylock, M. R., G. C. Cawley, C. Harpham, R. L. Wilby, and C. M. Goodess (2006), Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios, Int. J. Climatol., 26, 1397-1415. (Pubitemid 44193855)
25. Hewitson, B. (1994), Regional climates in the GISS general circulation model: Surface air temperature, J. Clim., 7(2), 283-303. (Pubitemid 24404926)
26. Hewitson, B. C., and R. G. Crane (1996), Climate downscaling: Techniques and application, Clim. Res., 7, 85-95.
27. Hornik, K. M. (1991), Approximation capabilities of multilayer feedforward networks, Neural Networks, 4, 251-257.
28. Hosking, J. R. M., J. R. Wallis, and E. F. Wood (1985), Estimation of the generalized extreme-value distribution by the method of probabilityweighted moments, Technometrics, 27, 251-261. (Pubitemid 15549855)
29. Huth, R. (2001), Disaggregating climatic trends by classification of circulation patterns, J. Climatol., 21, 135-153. (Pubitemid 32203428)
30. Huth, R. (2002), Statistical downscaling of daily temperature in central Europe, J. Clim., 15, 1731-1742.
31. Huth, R., S. Kliegrová, and L. Metelka (2008), Non-linearity in statistical downscaling: Does it bring an improvement for daily temperature in Europe?, J. Climatol., 28, 465-477. (Pubitemid 351454601)
32. Intergovernmental Panel on Climate Change (2007a), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge Univ. Press, Cambridge, U. K.
33. Intergovernmental Panel on Climate Change (2007b), Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by M. L. Parry et al., Cambridge Univ. Press, Cambridge, U. K.
34. Jolliffe, I. T. (1986), Principal Component Analysis, Springer, New York.
35. Kalnay, E., et al. (1996), The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77(3), 370-471.
36. Klein Tank, A. M. G., et al. (2002), Daily dataset of 20th-century surface air temperature and precipitation series for the European climate assessment, Int. J. Climatol., 22, 1441-1453. (Pubitemid 35288961)
37. Maraun, D., et al. (2010), Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, doi:10.1029/2009RG000314.
38. McLachlan, G., and D. Peel (2000), Finite Mixture Model, John Wiley, New York.
39. McNeil, A. J. (1997), Estimating the tails of loss severity distributions using extreme value theory, Astin Bull., 27, 117-137.
40. Nelsen, R. B. (2006), An Introduction to Copulas, Springer, New York.
41. Pickands, J. (1975), Statistical inference using extreme order statistics, Ann. Stat., 3, 119-131.
42. Priebe, C. E. (1994), Adaptive mixtures, J. Am. Stat. Assoc., 89, 796-806.
43. R Development Core Team (2010), R: A Language and Environment for Statistical Computing, R Found. for Stat. Comput., Vienna.
44. Rumelhart, D. E., G. E. Hinton, and R. J. Williams (1986), Learning internal representations by error propagation, in Parallel Distributed Processing: Explorations in the Macrostructure of Cognition, vol. 1, pp. 318-362, MIT Press, Cambridge, Mass.
45. Sailor, D. J., and X. Li (1999), A semi-empirical downscaling approach for predicting regional temperature impacts associated with climatic change, J. Clim., 12, 103-114. (Pubitemid 29253336)
46. Salameh, T., P. Drobinski, M. Vrac, and P. Naveau (2009), Statistical downscaling of near surface wind field over complex terrain in southern France, Meteorol. Atmos. Phys., 103, 253-265.
47. Schnur, R., and D. Lettenmaier (1998), A case study of statistical downscaling in Australia using weather classification by recursive partitioning, J. Hydrol., 212-213, 362-379. (Pubitemid 29020076)
48. Schwarz, G. (1978), Estimating the dimension of a model, Ann. Stat., 6, 461-464.
49. Semenov, M. A. (2007), Development of high-resolution UKCIP02-based climate change scenarios in the UK, Agric. For. Meteorol., 144, 127-138. (Pubitemid 46527963)
50. Semenov, M. A., and E.M. Barrow (1997), Use of a stochastic weather generator in the development of climate change scenarios, Clim. Res., 35, 397-414.
51. Semenov, M. A., R. J. Brooks, E. M. Barrow, and C. W. Richardson (1998), Comparison of the WGEN and the LARS-WG stochastic weather generators in diverse climates, Clim. Res., 10, 95-107. (Pubitemid 29126268)
52. Snell, S. E., S. Gopal, and R. K. Kaufmann (2000), Spatial interpolation of surface air temperatures using artificial neural networks: Evaluating their use for downscaling GCMs, J. Clim., 13, 886-895. (Pubitemid 30448549)
53. 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)
54. Vrac, M., M. Stein, and K. Hayhoe (2007a), Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing, Clim. Res., 34, 169-184. (Pubitemid 351736064)
55. Vrac, M., P. Marbaix, D. Paillard, and P. Naveau (2007b), Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe, Clim. Past, 3, 669-682. (Pubitemid 351269950)
56. Vrac, M., K. Hayhoe, and M. Stein (2007c), Identification and inter-model comparison of seasonal circulation patterns over North America, J. Climatol., 27, 603-620. (Pubitemid 46696393)
57. Wigley, T. M. L., P. D. Jones, K. R. Briffa, and G. Smith (1990), Obtaining sub-grid scale information from coarse resolution general circulation model output, J. Geophys. Res., 95, 1943-1953, doi:10.1029/JD095i D02p01943.
58. Wilby, R. L., C. W. Dawson, and E. M Barrow (2002), SDSM-A decision support tool for the assessment of regional climate change impacts, Environ. Modell. Software, 17(2), 145-157.
59. Wilks, D. S. (1999), Multisite downscaling of daily precipitation with a stochastic weather generator, Clim. Res., 11, 125-136. (Pubitemid 29257345)
60. 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. (Pubitemid 30005514)
61. Williams, M. P. (1998), Modelling seasonality and trends in daily rainfall data, Adv. Neural Inf. Process. Syst., 10, 985-991.
62. Yang, C., R. E. Chandler, and V. S. Isham (2005), Spatial-temporal rainfall simulation using generalized linear models, Water Resour. Res., 41, W11415, doi:10.1029/2004WR003739. (Pubitemid 43041784)