Seeking consensus: A new approach

Monthly Weather Review

Volumn 138, Issue 12, 2010, Pages 4402-4415

  Roebber, Paul J ^a  

^a UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

Forecasting; Model output statistics; Societal impacts; Surface observations; Surface temperature

Indexed keywords

ANNUAL COST; DATA SETS; ELECTRICITY PRODUCTION; FORECAST ACCURACY; FORECAST ALGORITHM; FORECAST ERRORS; LOGISTIC REGRESSION EQUATIONS; MEAN ABSOLUTE ERROR; MODEL OUTPUT STATISTICS; NATIONAL WEATHER SERVICES; NEW APPROACHES; SIMULATED EVOLUTIONS; SOCIETAL IMPACTS; STATE OF OHIO; SURFACE OBSERVATIONS; SURFACE TEMPERATURE; TEMPERATURE FORECASTS; WIND SPEED;

ALGORITHMS; ATMOSPHERIC TEMPERATURE; STATISTICAL TESTS; SURFACE PROPERTIES; WEATHER FORECASTING;

ELECTRIC LOAD FORECASTING;

ACCURACY ASSESSMENT; ALGORITHM; BENCHMARKING; FORECASTING METHOD; PROBABILITY; REGRESSION ANALYSIS; SURFACE TEMPERATURE; WEATHER FORECASTING; WIND VELOCITY;

OHIO; UNITED STATES;

EID: 78651108457     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010MWR3508.1     Document Type: Article

References (42)

1. Bakhshaii, A., and R. Stull, 2009: Deterministic ensemble forecasts using gene-expression programming. Wea. Forecasting, 24, 1431-1451.
2. Bosart, L. F., 1975: SUNYA experimental results in forecasting daily temperature and precipitation. Mon. Wea. Rev., 103, 1013-1020.
3. Bosart, L. F., 2003: Whither the weather analysis and forecasting process? Wea. Forecasting, 18, 520-529.
4. Brooks, H. E., M. S. Tracton, D. J. Stensrud, G. Dimego, and Z. Toth, 1995: Short-range ensemble forecasting: Report from a workshop, 25-27 July 1994.Bull. Amer. Meteor. Soc., 76,1617- 1624.
5. Cheng, S., J. Klaassen, N. Comer, H. Auld, D. MacIver, and A. Liu, 2001: The impacts of summer heat and air quality on energy and health in Ontario. Presentation to Sustainable Energy Futures for Central Ontario: The Impacts of Extreme Weather, Climate Change and a Changing Regulatory Environment; March 22, 2004, Toronto, ON, Canada, 9 pp. [Available online at http://www.pollutionprobe.org/Happening/pdfs/march22sustenergy/auld.pdf.]
6. Ferreira, C., 2006: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. 2nd ed. Springer, 478 pp.
7. Ferreira, C., cited 2010: GeneXproTools4.0: Modeling made easy. [Available online at http://www.gepsoft.com.]
8. Fritsch, J. M., J. Hilliker, J. Ross, and R. L. Vislocky, 2000: Model consensus. Wea. Forecasting, 15, 571-582.
9. Gorsuch, R. L., 1983: Factor Analysis. L. Erlbaum Associates, 425 pp.
10. Gyakum, J. R., 1986: Experiments in temperature and precipitation forecasting for Illinois. Wea. Forecasting, 1, 77-88.
11. Hagedorn, R., T. M. Hamill, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: Two-meter temperatures. Mon. Wea. Rev., 136, 2608-2619.
12. Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312-1327.
13. Hamill, T. M., and J. S. Whitaker, 2007: Ensemble calibration of 500-hPa geopotential height and 850-hPa and 2-m temperatures using reforecasts. Mon. Wea. Rev., 135, 3273-3280.
14. Hamill, T. M., J. S. Whitaker, and S. Mullen, 2006: Reforecasts: An important dataset for improving weather predictions. Bull. Amer. Meteor. Soc., 87, 33-46.
15. Hillis, W. D., 1990: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D, 42, 228-234.
16. Hobbs, B. F., S. Jitprapaikulsarn, S. Konda, V. Chankong, K. A. Loparo, and D. J. Maratukulam, 1999: Analysis of the value for unit commitment decisions of improved load forecasts. IEEE Trans. Power Syst., 14, 1342-1348.
17. Hotelling, H., 1933: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol., 24, 417-498.
18. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.
19. Klein, W. H., B. M. Lewis, and I. Enger, 1959: Objective prediction of five-day mean temperature during winter. J. Meteor., 16, 672-682.
20. Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhan, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from a multi-model superensemble. Science, 285, 1548-1550.
21. Mass, C. F., 2003: IFPS and the future of the National Weather Service. Wea. Forecasting, 18, 75-79.
22. McCollor, D., and R. Stull, 2009: Evaluation of probabilistic medium-range temperature forecasts from the North American ensemble forecast system. Wea. Forecasting, 24, 3-17.
23. Richardson, D. S., 2000: Skill and relative economic value of the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 126, 649-667.
24. Roebber, P. J., and L. F. Bosart, 1996: The complex relationship between forecast skill and forecast value: A real world analysis. Wea. Forecasting, 11, 544-559.
25. Roebber, P. J., L. F. Bosart, and G.S. Forbes, 1996: Does distance from the forecast site affect skill? Wea. Forecasting, 11, 582-589.
26. Roebber, P. J., D. M. Schultz, and R. Romero, 2002: Synoptic regulation of the 3 May 1999 tornado outbreak. Wea. Forecasting, 17, 399-429.
27. Rosenzweig, C., and W. Solecki, 2001: Climate change and a global city: The potential consequences of climate variability and change-Metro East Coast. Report for the U.S. Global Change Research Program, National Assessment of the Potential Consequences of Climate Variability and Change for the United States, Columbia Earth Institute, Columbia University.
28. Sanders, F., 1973: Skill in forecasting daily temperature and precipitation: Some experimental results. Bull. Amer. Meteor. Soc., 54, 1171-1178.
29. Sanders, F., 1979: Trends in skill of daily forecasts of temperature and precipitation, 1966-78. Bull. Amer. Meteor. Soc., 60, 763-769.
30. Stensrud, D. J., H. E. Brooks, J. Du, M. S. Tracton, and E. Rogers, 1999: Using ensembles for short-range forecasting. Mon. Wea. Rev., 127, 433-446.
31. Teisberg, T. J., R. F. Weiher, and A. Khotanzad, 2005: The economic value of temperature forecasts in electricity generation. Bull. Amer. Meteor. Soc., 86, 1765-1771.
32. Thompson, P. D., 1977: How to improve accuracy by combining independent forecasts. Mon. Wea. Rev., 105, 228-229.
33. Toth, Z., E. Kalnay, S. M. Tracton, R. Wobus, and J. Irwin, 1997: A synoptic evaluation of the NCEP ensemble. Wea. Forecasting, 12, 140-153.
34. Toth, Z., Y. Zhu, T. Marchok, M. S. Tracton, and E. Kalnay, 1998: Verification of the NCEP global ensemble forecasts. Preprints, 12th Conf. on Numerical Weather Prediction, Phoenix, AZ, Amer. Meteor. Soc., 286-289.
35. Tracton, S., J. Du, Z. Toth, and H. Juang, 1998: Short-range ensemble forecasting (SREF) at NCEP/EMC. Preprints, 12th Conf. on Numerical Weather Prediction, Phoenix, AZ, Amer. Meteor. Soc., 269-272.
36. Valor, E., V. Meneu, and V. Caselles, 2001: Daily air temperature and electricity load in Spain. J. Appl. Meteor., 40, 1413-1421.
37. Vislocky, R. L., and J. M. Fritsch, 1995: Improved model output statistics forecasts through model consensus. Bull. Amer. Meteor. Soc., 76, 1157-1164.
38. Vislocky, R. L., and J. M. Fritsch, 1997: Performance of an advanced MOS system in the 1996-97 National Collegiate Weather Forecasting Contest. Bull. Amer. Meteor. Soc., 78, 2851-2857.
39. Vukicevic, T., I. Jankov, and J. McGinley, 2008: Diagnosis and optimization of ensemble forecasts. Mon. Wea. Rev., 136, 1054-1074.
40. Weigel, A. P., M. A. Liniger, and C. Appenzeller, 2008: Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Quart. J. Roy. Meteor. Soc., 134, 241-260.
41. Zhang, F., C. Snyder, and R. Rotunno, 2002: Mesoscale predictability of the "surprise" snowstorm of 24-25 January 2000. Mon. Wea. Rev., 130, 1617-1632.
42. Zhu, Y., Z. Toth, R. Wobus, D. Richardson, and K. Mylne, 2002: The economic value of ensemble-based weather forecasts. Bull. Amer. Meteor. Soc., 83, 73-83.