Model-inspired predictors for model output statistics (MOS)

Monthly Weather Review

Volumn 135, Issue 10, 2007, Pages 3496-3505

  Termonia, Piet ^a   Deckmyn, Alex ^a  

^a ROYAL METEOROLOGICAL INSTITUTE OF BELGIUM   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC TEMPERATURE; HEAT FLUX; HEAT RADIATION; LATENT HEAT; LINEAR REGRESSION; MATHEMATICAL MODELS; PARAMETERIZATION; STATISTICS;

MODEL OUTPUT STATISTICS; SUBGRID PROCESSES;

WEATHER FORECASTING;

CLIMATE MODELING; DATA ASSIMILATION; ERROR ANALYSIS; LATENT HEAT FLUX; PARAMETERIZATION; PREDICTION; REGRESSION ANALYSIS; SENSIBLE HEAT FLUX; SOLAR RADIATION; THERMAL REGIME;

EID: 35748970485     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1029/MWR3469.1     Document Type: Article

References (20)

1. ALADIN International Team, 1997: The ALADIN project: Mesoscale modelling seen as a basic tool for weather forecasting and atmospheric research. WM0 Bull., 46, 317-324.
2. Baars, J. A., and C. F. Mass, 2005: Performance of National Weather Service forecasts compared to operational, consensus, and weighted model output statistics. Wea. Forecasting, 20, 1034-1047.
3. Best, M. J., A. Beljaars, J. Polcher, and P. Viterbo, 2004: A proposed structure for coupling tiled surfaces with the planetary boundary layer. J. Hydrometeor., 5, 1271-1278.
4. Brožková, R., M. Derková, M. Bellus, and A. Farda, 2006: Atmospheric forcing by ALADIN/MFSTEP and MFSTEP oriented tunings. Ocean Sci. Discuss., 3, 1-24.
5. Giard, D., and E. Bazile, 2000: Implementation of a new assimilation scheme for soil and surface variables in a global NWP model. Mon. Wea. Rev., 128, 997-1015.
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. Hart, K. A., W. J. Steenburgh, D. J. Onton, and A. J. Siffert, 2004: An evaluation of mesoscale-model-based output statistics (MOS) during the 2002 Olympic and Paralympic winter games. Wea. Forecasting, 19, 200-218.
8. Kalnay, E., 2003: Atmospheric Modelling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.
9. Katz, R. W., and A. H. Murphy, Eds., 1997: Economic Value of Weather and Climate Forecasts. Cambridge University Press, 222 pp.
10. Marzban, C., 2003: Neural networks for postprocessing model output: ARPS. Mon. Wea. Rev., 131, 1103-1111.
11. Marzban, C., S. Sandgathe, and E. Kalnay, 2006: MOS, perfect prog, and re-analysis. Mon. Wea. Rev., 134, 657-663.
12. Sokol, Z., 2003: MOS-based precipitation forecasts for river basins. Wea. Forecasting, 18, 769-781.
13. Taylor, A. A., and L. M. Leslie, 2005: A single-station approach to model output statistic temperature forecast error assessment. Wea. Forecasting, 20, 1006-1020.
14. Termonia, P., 2001: On the removal of random variables in data sets of meteorological observations. Meteor. Atmos. Phys., 78, 143-156.
15. 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.
16. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.
17. Wilson, L. J., and M. Vallée, 2002: The Canadian updateable model output statistics (UMOS) system: Design and development tests. Wea. Forecasting, 17, 206-222.
18. Yuval, and W. W. Hsieh, 2003: An adaptive nonlinear scheme for precipitation forecasts using neural networks. Wea. Forecasting, 18, 303-310.
19. Zwiers, F. W., 1987: Statistical considerations for climate experiments. Part II: Multivariate tests. J. Climate Appl. Meteor., 26, 477-487.
20. Zwiers, F. W., 1990: The effect of serial correlation on statistical inferences made by resampling procedures. J. Climate, 3, 1452-1461.