On the proper order of Markov chain model for daily precipitation occurrence in the contiguous United States

Journal of Applied Meteorology and Climatology

Volumn 47, Issue 9, 2008, Pages 2477-2486

  Schoof, J T ^a   Pryor, S C ^b  

^a SOUTHERN ILLINOIS UNIVERSITY   (United States)

^b INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DROUGHT; IMAGE SEGMENTATION; MARKOV PROCESSES; PROBABILITY DISTRIBUTIONS;

BAYESIAN INFORMATIONS; DRY SPELLS; FIRST ORDERS; KOLMOGOROV; LENGTH DISTRIBUTIONS; MARKOV CHAIN MODELS; MARKOV CHAINS; MARKOV MODELS; MAXIMUM-LIKELIHOOD; MODEL ORDERS; TRANSITION PROBABILITIES; UNITED STATES; WET AND DRIES;

MATHEMATICAL MODELS;

BAYESIAN ANALYSIS; BOOTSTRAPPING; ESTIMATION METHOD; MARKOV CHAIN; MAXIMUM LIKELIHOOD ANALYSIS; MODELING; NUMERICAL MODEL; PRECIPITATION ASSESSMENT; PROBABILITY;

NORTH AMERICA; UNITED STATES;

EID: 57649174271     PISSN: 15588424     EISSN: None     Source Type: Journal    
DOI: 10.1175/2008JAMC1840.1     Document Type: Article

References (28)

1. Akaike, H., 1974: A new look at the statistical model identification. IEEE Trans. Autom. Control, 19, 716-723.
2. Buishand, T. A., 1978: Some remarks on the use of daily rainfall models. J. Hydrol., 36, 295-308.
3. Chin, E. H., 1977: Modeling daily precipitation occurrence process with Markov chain. Water Resour. Res., 13, 949-956.
4. Dobi-Wantuch, I., J. Mika, and L. Szeidl, 2000: Modelling wet and dry spells with mixture distributions. Meteor. Atmos. Phys., 73, 245-256.
5. Efron, B., 1982: The Jackknife, the Bootstrap and Other Resampling Plans. CBMS-NSF Regional Conference Series in Applied Mathematics, No. 38, Society for Industrial and Applied Mathematics, 92 pp.
6. Gabriel, K. R., and J. Neumann, 1962: A Markov chain model for daily rainfall occurrence at Tel Aviv. Quart. J. Roy. Meteor. Soc., 88, 90-95.
7. Guttorp, P., 1995: Stochastic Modeling of Scientific Data. Chapman and Hall, 384 pp.
8. Harrison, M., and P. Waylen, 2000: A note concerning the proper choice for Markov model order for daily precipitation in the humid tropics: A case study in Costa Rica. Int. J. Climatol., 20, 1861-1872.
9. Hayhoe, H. N., 2000: Improvements of stochastic weather data generators for diverse climates. Climatic Res., 14, 75-87.
10. Johnson, G. L., C. Daly, G. H. Taylor, and C. L. Hanson, 2000: Spatial variability and interpolation of stochastic weather simulation model parameters. J. Appl. Meteor., 39, 778-796.
11. Katz, R. W., 1977: Precipitation as a chain-dependent process. J. Appl. Meteor., 16, 671-676.
12. Katz, R. W., 1981: On some criteria for estimating the order of a Markov chain. Technometrics, 23, 243-249.
13. Katz, R. W., and M. B. Parlange, 1998: Overdispersion phenomenon in stochastic modeling of precipitation. J. Climate, 11, 591-601.
14. Kottegoda, N. T., L. Natale, and E. Raiteri, 2004: Some considerations of periodicity and persistence in daily rainfalls. J. Hydrol., 296, 23-37.
15. Lowry, W. P., and D. Guthrie, 1968: Markov chains of order greater than one. Mon. Wea. Rev., 96, 798-801.
16. Nicks, A. D., and G. A. Gander, 1994: CLIGEN: A weather generator for climate inputs to water resource and other models. Proc. Fifth Int. Conf. on Computers in Agriculture, Orlando, FL, American Society of Agricultural Engineers, 903-909.
17. Racsko, P., L. Szeidl, and M. Semenov, 1991: A serial approach to local stochastic weather models. Ecol. Modell., 57, 27-41.
18. Richardson, C. W., 1981: Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res., 17, 182-190.
19. Richardson, C. W., and D. A. Wright, 1984: WGEN: A model for generating daily weather variables. U.S. Department of Agriculture, Agriculture Research Service Rep. ARS-8, 83 pp.
20. Roldán, J., and D. A. Woolhiser, 1982: Stochastic daily precipitation models. 1. A comparison of occurrence processes. Water Resour. Res., 18, 1451-1459.
21. Schoof, J. T., A. Arguez, J. Brolley, and J. J. O'Brien, 2005: A new weather generator based on spectral properties of surface air temperatures. Agric. For. Meteor., 135, 241-251.
22. Schwarz, G., 1978: Estimating the dimension of a model. Ann. Stat., 6, 461-464.
23. Semenov, M. A., and E. M. Barrow, 1997: Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 35, 397-414.
24. Wallis, T. W. R., and J. F. Griffiths, 1995: An assessment of the weather generator (WXGEN) used in the erosion/productivity impact calculator (EPIC). Agric. For. Meteor., 73, 115-133.
25. Wan, H., X. Zhang, and E. M. Barrow, 2005: Stochastic modelling of daily precipitation for Canada. Atmos.-Ocean, 43, 23-32.
26. Wilks, D. S., 1992: Adapting stochastic weather generation algorithms for climate change studies. Climatic Change, 22, 67-84.
27. Wilks, D. S., 1999: Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agric. For. Meteor., 93, 153-169.
28. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 648 pp.