Comparative analysis of the local observation-based (LOB) method and the nonparametric regression-based method for gridded bias correction in mesoscale weather forecasting

Weather and Forecasting

Volumn 22, Issue 6, 2007, Pages 1243-1256

  Gel, Yulia R ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC TEMPERATURE; COMPUTER SIMULATION; ENGINEERING RESEARCH; MATHEMATICAL MODELS; METEOROLOGY; OCEANOGRAPHY; REGRESSION ANALYSIS; STATISTICS;

ALTERNATIVE CONDITIONAL EXPECTATION; ATMOSPHERIC RESEARCH MESOSCALE MODEL; CLASSIFICATION AND REGRESSION TREES; MODEL OUTPUT STATISTICS;

WEATHER FORECASTING;

CLIMATE PREDICTION; COMPARATIVE STUDY; CORRECTION; MESOSCALE METEOROLOGY; NUMERICAL METHOD; OBSERVATIONAL METHOD; REGRESSION ANALYSIS; WEATHER FORECASTING;

NORTH AMERICA; PACIFIC OCEAN; PACIFIC OCEAN (NORTHWEST); PENNSYLVANIA; UNITED STATES;

EID: 38849101652     PISSN: 08828156     EISSN: None     Source Type: Journal    
DOI: 10.1175/2007WAF2006046.1     Document Type: Article

References (33)

1. Applequist, S., G. E. Gahrs, and R. L. Pfeffer 2002: Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecasting, 17, 783-799.
2. Breiman, L., and J. H. Friedman, 1985: Estimating optimal transformations for multiple regression and correlations (with discussion). J. Amer. Stat. Assoc., 80, 580-619.
3. Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone, 1984: Classification and Regression Trees. Wadsworth and Brooks/Cole, 358 pp.
4. Buja, A., and R. E. Kass, 1985: Estimating optimal transformations for multiple regression and correlation: Comment. J. Amer. Stat. Assoc., 80, 602-607.
5. Burrows, W. R., M. Benjamin, S. Beauchamp, E. R. Lord, D. Mc-Collor, and B. Thompson, 1995: CART decision-tree statis-tical analysis and prediction of summer season maximum surface ozone for Vancouver, Montreal, and Atlantic regions of Canada. J. Appl. Meteor., 34, 1848-1862.
6. Christy, J. R., 2002: When was the hottest summer? A state climatologist struggles for an answer. Bull. Amer. Meteor. Soc., 83, 723-734.
7. Cressie, N., 1993: Statistics for Spatial Data. Wiley, 928 pp.
8. Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.
9. Fielding, A. H., 1999: Ecological Applications of Machine Learning Methods. Kluwer Academic, 280 pp,
10. Gel, Y., A. E. Raftery, and T. Gneiting, 2004: Combining global and local grid-based bias correction for mesoscale numerical weather prediction models. Preprints, 17th Conf. on Probability and Statistics in the Atmospheric Sciences, Seattle, WA, Amer. Meteor. Soc., CD-ROM, 1.9.
11. Glahn, H. R., 1985: Statistical weather forecasting. Probability, Statistics, and Decision Making in the Atmospherics Sciences, A. R. Murphy and R. W. Katz, Eds., Westview Press, 289-335.
12. 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.
13. Hastie, T. J., and R. J. Tibshirani, 1990: Generalized Additive Models. CRC Press, 352 pp.
14. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re-analysis Project. Bull. Amer. Meteor. Soc., 77, 437-471.
15. Krivoruchko, K., and A. Gribov, 2004: Geostatistical interpolation and simulation with mon-Euclidean distances. GeoENV IV - Geostatistis tics for Environmental Applications, X. Sanchez-Villa, J. Carrera, and J. I Gomez-Hernandez, Eds., Kluwer Academic, 331-342.
16. Kuligowski, R. J., and A. P. Barros, 1998: Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Wea. Forecasting, 13, 1194-1204.
17. Mao, Q., R. T. McNider, S. F. Mueller, and H.-M. H. Juang, 1999: An optimal model output calibration algorithm suitable for objective temperature forecasting. Wea. Forecasting, 14, 190-202.
18. Marzban, C., 2003: Neural networks for postprocessing model output: ARPS. Mon. Wea. Rev., 131, 1103-1111.
19. Mass, C. F., 2003: IFPS and the future of the National Weather Service. Wea. Forecasting, 18, 75-79.
20. Mass, C. F., and Y. H. Kuo, 1998: Regional real-time numerical weather prediction: Current status and future potential. Bull. Amer. Meteor. Soc., 79, 253-263.
21. Mass, C. F., and Coauthors, 2003: Regional environmental prediction over the Pacific Northwest. Bull. Amer. Meteor. Soc., 84, 1353-1366.
22. Nott, D. J., W. T. M. Dunsmuir, R. Kohn, and F. Woodcock, 2001: Statistical correction of a deterministic weather prediction model. J. Amer. Stat. Assoc., 96, 794-804.
23. Pielke, R. A., Sr., T. Stohlgren, W. Parton, N. Doesken, J. Moeny, L. Schell, and K. Redmond, 2000: Spatial representativeness of temperature measurements from a single site. Bull. Amer. Meteor. Soc., 81, 826-830.
24. Spark, E., and G. J. Connor, 2004: Wind forecasting for the sailing events at the Sydney 2000 Olympic and Paralympic Games. Wea. Forecasting, 19, 181-199.
25. Stensrud, D. J., and J. A. Skindlov, 1996: Gridpoint predictions of high temperature from a mesoscale model. Wea. Forecasting, 11, 103-110.
26. Stensrud, D. J., and N. Yussouf, 2003: Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev., 131, 2510-2524.
27. Tebaldi, C., 2002: Looking far back vs. looking around enough: Operational weather forecasting by spatial composition of recent observations. Preprints, 16th Conf on Probability and Statistics in the Atmospheric Sciences, Orlando, FL, Amer. Meteor. Soc., CD-ROM, 3.8.
28. Vislocky, R. L., and J. M. Fritsch, 1995: Generalized additive models versus linear regression in generating probabilistic MOS forecasts of aviation weather parameters. Wea. Forecasting, 10, 669-680.
29. Wedam, G., C. F. Mass, and R. Steed, 2005: Grid-based bias removal of surface parameters. Pacific Northwest Weather Workshop, Seattle, WA, University of Washington and NWS. [Available online at http:// www.wrh.noaa.gov/sew/WorkShop_05/session_3/Wedam.pdf.]
30. Weisberg, S., 1985: Applied Linear Regression. Wiley, 324 pp.
31. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.
32. Wilson, L. J., and M. Vallee, 2002: The Canadian Updateable Model Output Statistics (UMOS) system: Design and development tests. Wea. Forecasting, 17, 206-222.
33. Yuval, and W. H. Hsieh, 2003: An adaptive nonlinear MOS scheme for precipitation forecasts using neural networks. Wea. Forecasting, 18, 303-310.