The effects of imputing missing data on ensemble temperature forecasts

Journal of Computers

Volumn 6, Issue 2, 2011, Pages 162-171

  McCandless, Tyler C ^a   Haupt, Sue Ellen ^a,b   Young, George S ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

Artificial intelligence (AI); Artificial neural network (ANN); Data imputation; Ensemble forecasting; Missing data; Numerical weather prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORK (ANN); DATA IMPUTATION; ENSEMBLE FORECASTING; MISSING DATA; NUMERICAL WEATHER PREDICTION;

CALIBRATION; GRAPHIC METHODS; NEURAL NETWORKS; PROCESSING; STATISTICAL METHODS;

WEATHER FORECASTING;

EID: 79951756342     PISSN: 1796203X     EISSN: None     Source Type: Journal    
DOI: 10.4304/jcp.6.2.162-171     Document Type: Article

References (26)

1. Kidson, J.W., and K.E. Trenberth, 1988: Effects of missing data on estimates of monthly mean general circulation statistics, J. Climate, 1, 1261-1275.
2. Vincent, L. A., and D. W. Gullet., 1999: Canadian historical and homogeneous temperature datasets for climate change analyses. International Journal of Climatology. 19, 1375-1388.
3. Schneider, T., 2001: Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J. Climate, 14, 853-871
4. Richman, M. B., R. B. Trafalis, and I. Adrianto., 2009: Missing data imputation through machine learning algorithms. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer-Verlag, 153-169.
5. Kemp, W. P., D. G. Brunell, D.O. Everson, and A. J. Thomson, 1983. Estimating missing daily maximum and minimum temperatures. J. Climate, 22, 1587-1593.
6. Glahn, H. R., and D. A. Lowry, 1972: The use of Model Output Statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 1203-1211.
7. Hamill, T. M., S.L. Mullen, C. Snyder, Z. Toth, and D.P. Baumhefner, 2000: Ensemble forecasting in the short to medium range: report from a workshop, Bull. Amer. Meteor. Soc., 81, 2653-2664.
8. Woodcock, F. and C. Engel, 2005: Operational consensus forecasts. Wea. Forecasting 20, 101-111.
9. Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles, Mon. Wea. Rev., 133, 1155-1174.
10. Fraley, C., A.E. Raftery, and T. Gneiting, 2010: Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian Model Averaging, Mon. Wea. Rev., 138, 190-202.
11. Greybush, S. J., S.E. Haupt., and G. S. Young., 2008: The regime dependence of optimally weighted ensemble model consensus forecasts of surface temperature. Wea. Forecasting, 23, 1146-1161.
12. Grimit, E.P. and C.F. Mass, 2002: Aspects of effective mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17, 192-205.
13. Rubin, D.B. (1976) Inference and missing data. Biometrika, 63, 581-592.
14. Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data, 2nd edition, New York: John Wiley.
15. Witten, I. H., and E. Frank., 2005: Data mining: practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
16. Krasnopolsky, V. M., 2009: Neural network applications to solve forward and inverse problems in atmospheric and oceanic satellite remote sensing. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer-Verlag, 191-205.
17. Young, G. S., 2009: Implementing a neural network emulation of a satellite retrieval algorithm. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer-Verlag, 207-216.
18. Wilks, D.S., 2005: Statistical methods in the atmospheric sciences, 2nd ed., Academic Press, 626 pp.
19. Anderson, J. L., 1996: A method for producing and evaluating probabilistic forecasts from Ensemble Model Integrations. J. Climate, 9, 1518-1530.
20. Hamill, T. M., and S. J. Colucci, 1996: Random and systematic error in NMC's short-range Eta ensembles. Preprints, 13th Conf. on Probability and Statistics in the Atmospheric Sciences, San Francisco, CA, Amer. Meteor. Soc., 51-56.
21. Talagrand, O., R. Vautard, and B. Strauss, 1997: Evaluation of probabilistic prediction systems. Proceedings, ECMWF Workshop on Predictability, ECMWF, 1-25.
22. 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550-560.
23. Marzban, C., R. Wang, F. Kong, S. Leyton, 2010: On the effect of correlations on rank histograms: Reliability of temperature and wind-speed forecasts from fine-scale ensemble reforecasts, Mon. Wea. Rev., in press.
24. Saetra, O, H. Hersbach, J-R Bidlot and D.S. Richardson, 2004: Effects of observation errors on the statistics for ensemble spread and reliability. Mon. Wea. Rev., 132, 1487-1501.
25. Elmore, K.L., 2005: Alternatives to the chi-square test for evaluating rank histograms from ensemble forecasts. Wea. Forecasting, 20, 789-795.
26. Jolliffe, I. T., and C. Primo, 2008: Evaluating rank histograms using decomposition of the chi-square test statistic, Mon. Wea. Rev., 136, 2133-2139.