Probabilistic precipitation forecasting based on ensemble output using generalized additive models and bayesian model averaging

Acta Meteorologica Sinica

Volumn 26, Issue 1, 2012, Pages 1-12

  Yang, Chi ^a,b   Yan, Zhongwei ^b   Shao, Yuehong ^b  

^a BEIJING NORMAL UNIVERSITY   (China)

^b INSTITUTE OF ATMOSPHERIC PHYSICS   (China)

Author keywords

Bayesian model averaging; Generalized additive model; Probabilistic precipitation forecasting; TIGGE; Tweedie distribution

Indexed keywords

ALGORITHM; BAYESIAN ANALYSIS; CLIMATE CHANGE; DOWNSCALING; ENSEMBLE FORECASTING; NUMERICAL MODEL; PRECIPITATION ASSESSMENT; PROBABILITY; SPATIAL VARIATION; TEMPORAL VARIATION;

CHINA; YISHU RIVER;

EID: 84863252959     PISSN: 08940525     EISSN: None     Source Type: Journal    
DOI: 10.1007/s13351-012-0101-8     Document Type: Article

References (23)

1. Chandler, R. E., and H. S. Wheater, 2002: Analysis of rainfall variability using generalized linear models: a case study from the west of Ireland. Water Resource Res., 38(10), 1192-1202.
2. Coe, R., and R. D. Stern, 1982: Fitting models to daily rainfall data. J. Appl. Meteor., 21, 1024-1031.
3. Dunn, P. K., 2004: Occurrence and quantity of precipitation can be modeled simultaneously. Int. J. Climatol., 24, 1231-1239.
4. Dunn, P. K., 2009: Tweedie: Tweedie Exponential Family Models. R package version 2.0.2, http://CRAN.Rproject. org/package=tweedie.
5. Gneiting, T., and A. E. Raftery, 2007: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc., 102, 359-378.
6. Hastie, T. J., and R. J. Tibshirani, 1990: Generalized Additive Models. Chapman and Hall, London, 335 pp.
7. Jolliffe, I. T., and D. B. Stephenson, 2003: Forecast Verification: A Practitioner's Guide in Atmospheric Science. Wiley and Sons, 240 pp.
8. Markatou, M., 2000: Mixture models, robustness, and the weighted likelihood methodology. Biometrics, 56(2), 483-486.
9. McLachlan, G. J., and D. Peel, 2000: Finite Mixture Models. Wiley, 419 pp.
10. McLachlan, G. J., and T. Krishnan, 2008: The EM Algorithm and Extensions. 2nd Edition. Wiley, 359 pp.
11. McCullagh, P., and J. A. Nelder, 1989: Generalized Linear Models. 2nd Edition, Chapman and Hall, London, 532 pp.
12. Park, Y.-Y., R. Buizza, and M. Leutbecher, 2008: TIGGE: preliminary results on comparing and combining ensembles. Quart. J. Roy. Meteor. Soc., 134, 2029-2050.
13. R Development Core Team, 2011: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.Rproject. org.
14. Raftery, A. E., T. Gneiting, F. Balabdaoui, et al., 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155-1174.
15. Ruppert, D., M. P. Wand, and R. J. Carroll, 2003: Semiparametric Regression. Cambridge University Press, 386 pp.
16. Sloughter, J. M., A. E. Raftery, T. Gneiting, et al., 2007: Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon. Wea. Rev., 135, 3209-3220.
17. Stern, R. D., and R. Coe, 1984: A model fitting analysis of daily rainfall data. J. Roy. Stat. Soc., 147A, 1-34.
18. Tweedie, M. C. K., 1984: An index which distinguishes between some important exponential families in statistics: Applications and new directions. Proceedings of the Indian Statistical Institute Golden Jubilee International Conference, J. K. Ghosh and J. Roy, Eds., Indian Statistical Institute, Calcutta, 579-604.
19. Wood, S. N., 2003: Thin plate regression splines. J. R. Statist. Soc. B, 65(1), 95-114.
20. Wood, S. N., 2006: Generalized Additive Models: An Introduction with R. CRC/Chapman & Hall, Boca Raton, Florida, 392 pp.
21. Wood, S. N., and N. H. Augustin, 2002: GAMs with integrated model selection using penalized regression splines and applications to environmental modeling. Eco. Modelling, 157, 157-177.
22. Yang, C., R. E. Chandler, V. S. Isham, et al., 2005: Spatial-temporal rainfall simulation using generalized linear models. Water Resource Res., 41(11), W11415.
23. Zhu, Y., 2005: Ensemble forecast: A new approach to uncertainty and predictability. Adv. Atmos. Sci., 22(6), 781-788.