Dynamical properties of model output statistics forecasts

Monthly Weather Review

Volumn 136, Issue 2, 2008, Pages 405-419

  Vannitsem, S ^a   Nicolis, C ^a  

^a ROYAL METEOROLOGICAL INSTITUTE OF BELGIUM   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE MATRIX; MATHEMATICAL MODELS; METEOROLOGY; PHASE SPACE METHODS; STATISTICAL METHODS;

MODEL OUTPUT STATISTICS;

WEATHER FORECASTING;

ANALYTICAL METHOD; ATMOSPHERIC CORRECTION; DYNAMIC PROPERTY; FORECASTING METHOD; MODELING; NUMERICAL METHOD; PREDICTION;

EID: 44449178322     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2007MWR2104.1     Document Type: Article

References (33)

1. Agee, E. M., 1984: Observations from space and thermal convection: A historical perspective. Bull. Amer. Meteor. Soc., 65, 938-949.
2. Buizza, R., P. L. Houtekamer, G. Pellerin, Z. Toth, Y. Zhu, and M. Wei, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea Rev., 133, 1076-1097.
3. Casaioli, M., R. Mantovani, F. Proietti Scorzoni, S. Puca, A. Speranza, and B. Tirozzi, 2003: Linear and nonlinear post-processing of numerically forecasted surface temperature. Nonlinear Processes Geophys., 10, 373-383.
4. Dallavalle, J. P., M. C. Erickson, and J. C. Maloney III, 2004: Model output statistics (MOS) guidance for short-range projections. Preprints, 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 6.1.
5. D'Andrea, F., and R. Vautard, 2000: Reducing systematic errors by empirically correcting model errors. Tellus, 52A, 21-41.
6. Danforth, C. M., E. Kalnay, and T. Miyoshi, 2007: Estimating and correcting global weather model error. Mon. Wea. Rev., 135, 281-299.
7. Gilmour, I., L. A. Smith, and R. Buizza, 2001: On the duration of the linear regime: Is 24 hours a long time in weather forecasting? J. Atmos. Sci., 58, 3525-3539.
8. 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.
9. Gneiting, T., A. E. Raftery, A. H. Westveld III, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098-1118.
10. 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.
11. Hart, K. A., W. J. Steenburgh, D. J. Onton, and A. J. Siffert, 2004: An evaluation of mesoscale-model-based model output statistics (MOS) during the 2002 Olympic and Paralympic Winter Games. Wea. Forecasting, 19, 200-218.
12. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.
13. Klein, W. H., and H. R. Glahn, 1974: Forecasting local weather by means of model output statistics. Bull Amer. Meteor. Soc., 55, 1217-1227.
14. Kuramoto, Y., and T. Tsuzuki, 1976: Persistent propagation of concentration waves in dissipative media far from equilibrium. Prog. 7heor. Phys., 55, 356-369.
15. Leith, C. E., 1978: Objective methods for weather prediction. Annu. Rev. Fluid Mech., 10, 107-128.
16. Lemcke, C., and S. Kruizinga, 1988: Model output statistics forecasts: Three years of operational experience in the Netherlands. Mon. Wea. Rev., 116, 1077-1090.
17. Lorenz, E., 1984: Irregularity: A fundamental property of the atmosphere. Tellus, 36A, 98-110.
18. Manneville, P., 1990: Dissipative Structures and Weak Turbulence. Academic Press, 367 pp.
19. Marzban, C., 2003: Neural networks for postprocessing model outputs: ARPS. Mom Wea Rev., 131, 1103-1111.
20. Marzban, C., S. Sandgathe, and E. Kalnay, 2006: MOS, perfect prog, and reanalysis. Mon. Wea. Rev., 134, 657-663.
21. Nicolis, C., 1992: Probabilistic aspects of error growth in atmospheric dynamics. Quart. J. Roy. Meteor. Soc., 118, 553-568.
22. Nicolis, C., 2003: Dynamics of model error: Some generic features. J. Atmos. Sci., 60, 2208-2218.
23. Nicolis, C., 2004: Dynamics of model error: The role of unresolved scales revisited. J. Atmos. Sci., 61, 1740-1753.
24. Nicolis, C., 2005: Can error source terms in forecasting models be represented as Gaussian Markov noises? Quart. J. Roy. Meteor. Soc., 131, 2151-2170.
25. Nicolis, C., S. Vannitsem, and J.-F. Royer, 1995: Short range predictability of the atmosphere and Lyapunov exponents. Quart. J. Roy. Meteor. Soc., 121, 705-722.
26. Sokol, Z., 2003: MOS-based precipitation forecasts for river basins. Wea. Forecasting, 18, 769-781.
27. Taylor, A. A., and L. M. Leslie, 2005: A single-station approach to model output statistics temperature forecast error assessment. Wea. Forecasting, 20, 1006-1020.
28. Teixeira, J., C. Reynolds, and K. Judd, 2007: Time step sensitivity of nonlinear atmospheric models: Numerical convergence, truncation error growth, and ensemble design. J. Atmos. Sci., 64, 175-189.
29. Termonia, P., and A. Deckmyn, 2007: Model-inspired predictors for model output statistics (MOS). Mon. Wea. Rev., 135, 3496-3505.
30. Vannitsem, S., and C. Nicolis, 1994: Predictability experiments on a simplified thermal convection model: The role of spatial scales. J. Geophys. Res., 99, 10377-10385.
31. Vannitsem, S., and Z. Toth, 2002: Short-term dynamics of model errors. J. Atmos. Sci., 59, 2594-2604.
32. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Academic Press, 464 pp.
33. Wilson, L. J., and M. Vallée, 2003: The Canadian Updateable Model Output Statistics (UMOS) system: Validation against perfect prog. Wea. Forecasting, 18, 288-302.