Monte Carlo based ensemble forecasting

Statistics and Computing

Volumn 11, Issue 3, 2001, Pages 269-275

  Berliner, L Mark ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

Chaos; Importance sampling; Kernel density estimation; Mixtures; Monte carlo; Numerical weather forecasting; Tangent linear approximation

Indexed keywords

EID: 0042283657     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1016656422040     Document Type: Article

References (18)

1. Anderson J.L. 1996. Selection of initial conditions for ensemble forecasts in a simple perfect model framework. Journal of the Atmospheric Sciences 53: 22-36.
2. Berliner L.M. 1992. Statistics, probability, and chaos. Statistical Science 7: 69-122 (with Discussion).
3. Casella G. and Berger R.L. 1990. Statistical Inference. Wadsworth, Belmont, CA.
4. Ehrendorfer M. 1994. The Liouville equation and its potential usefulness for the prediction of forecast skill. Part I: Theory. Monthly Weather Review 122: 703-713.
5. Ehrendorfer M. and Tribbia J.J. 1996. Optimal prediction of forecast error covariances through singular vectors. Journal of the Atmospheric Sciences 54: 286-313.
6. Geweke J.F. 1989. Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57: 1317-1340.
7. Geweke J.F. 1996. Monte Carlo simulation and numerical integration. In: H. Amman, D. Kendrick, and J. Rust (Eds.), Handbook of Computational Economics North Holland, Amsterdam.
8. Givens G.H. and Raftery A.E. 1996. Local adaptive importance sampling for multivariate densities with strong nonlinear relationships. Journal of the American Statistical Association 91: 132-141.
9. Johnson R.A. and Wichern D.W. 1982. Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs, NJ.
10. Kalnay E.M., Kanamitsu M., and Baker W.E. 1990. Global numerical weather prediction at the National Meteorological Center. Bulletin of the American Meteorological Society 71: 1410-1428.
11. Oh M.S. and Berger J.O. 1992. Adaptive importance sampling in Monte Carlo integration. Journal of Statistical Computation and Simulation 41: 143-168.
12. Ripley B.D. 1987. Stochastic Simulation. John Wiley & Sons, New York.
13. Silverman B.W. 1986. Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.
14. Smith L.A. 1994. Visualising predictability with chaotic ensembles. In: F.T. Luk (Ed.), Advanced Signal Processing: Algorithms, Architectures, and Implementations. SPIE Proc. 2296, SPIE Press, Bellingham, WA.
15. Titterington D.M. 1985. Common structure of smoothing techniques in statistics. International Statistical Review 53: 141-170.
16. Toth Z. and Kalnay E. 1993. Ensemble forecasting at NMC: The generation of perturbations. Bulletin of the American Meteorological Society 74: 2317-2330.
17. West M. 1992. Modelling with mixtures. In: J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith (Eds.), Bayesian Statistics 4 Oxford University Press, Oxford.
18. West M. 1993. Approximating posterior distributions by mixtures. Journal of the Royal Statistical Society Series B 55: 409-422.