A unified linear Model Output Statistics scheme for both deterministic and ensemble forecasts

Quarterly Journal of the Royal Meteorological Society

Volumn 135, Issue 644, 2009, Pages 1801-1815

  Vannitsem, S ^a  

^a ROYAL METEOROLOGICAL INSTITUTE OF BELGIUM   (Belgium)

Author keywords

EVMOS; NGR; Post processing

Indexed keywords

ATMOSPHERIC MODEL; CLASSICAL TECHNIQUES; ENSEMBLE FORECASTS; EVMOS; GAUSSIAN REGRESSION; LEAST SQUARE; LINEAR MODEL; LONG LEADS; MODEL ERRORS; NEW APPROACHES; NGR; NON-HOMOGENEOUS; POST-PROCESSING; PROBABILISTIC FORECASTS;

CHAOTIC SYSTEMS; COST FUNCTIONS; ERRORS; PARAMETER ESTIMATION;

WEATHER FORECASTING;

ATMOSPHERIC MODELING; ENSEMBLE FORECASTING; ESTIMATION METHOD; GAUSSIAN METHOD; LEAST SQUARES METHOD; OBSERVATIONAL METHOD; PARAMETERIZATION; REGRESSION ANALYSIS;

EID: 70350629740     PISSN: 00359009     EISSN: None     Source Type: Journal    
DOI: 10.1002/qj.491     Document Type: Article

References (20)

1. Buizza R, Houtekamer PL, Pellerin G, Toth Z, Zhu Y, Wei M. 2005. A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Weather Rev. 133: 1076-1097.
2. Casella G, Berger RL. 2002. Statistical Inference. Duxbury Advanced Series, Thomson Learning.
3. Crone LJ, McMillin LM, Crosby DS. 1996. Constrained regression in satellite meteorology. J. Appl. Meteorol. 35: 2023-2035.
4. Doblas-Reyes FJ, Hagedorn R, Palmer TN. 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting-II Calibration and combination. Tellus 57A: 234-252.
5. Klein WH, Glahn HR. 1974. Forecasting local weather by means of model output statistics. Bull. Am. Meteorol. Soc. 55: 1217-1227.
6. Lemcke C, Kruizinga S. 1988. Model Output Statistics forecasts: Three years of operational experience in the Netherlands. Mon. Weather Rev. 116: 1077-1090.
7. Lorenz E. 1984. Irregularity: A fundamental property of the atmosphere. Tellus 36A: 98-110.
8. Markovsky I, Van Huffel S. 2007. Overview of total least squares methods. Signal Processing 87: 2283-2302.
9. Molteni F, Buizza R, Palmer TN, Petroliagis T. 1996. The ECMWF ensemble prediction system: Methodology and validation. Q. J. R. Meteorol. Soc. 122: 73-119.
10. Nicolis C. 2004. Dynamics of model error: the role of unresolved scales revisited. J. Atmos. Sci. 61: 1740-1753.
11. Press WH, Teukolsky SA, Vetterling WT, Flannery BP. 1996. Numerical Recipes in Fortran 77. Cambridge University Press: Cambridge, UK.
12. Roulston MS, Smith LA. 2003. Combining dynamical and statistical ensembles. Tellus 55A: 16-30.
13. Stephenson DB, Coelho CAS, Balmaseda M, Doblas-Reyes FJ. 2005. Forecast assimilation: A unified framework for the combination of multi-model weather and climate predictions. Tellus 57A: 253-264.
14. Toth Z, Kalnay E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Weather Rev. 125: 3297-3319.
15. Van Huffel S, Vandewalle J. 1991. The total least squares problem: Computational aspects and analysis. SIAM: Philadelphia, USA.
16. Vannitsem S. 2008. Dynamical properties of MOS forecasts: Analysis of the ECMWF operational forecasting system. Weather and Forecasting 23: 1032-1043.
17. Vannitsem S, Nicolis C. 2008. Dynamical properties of Model Output Statistics forecasts. Mon. Weather Rev. 136: 405-419.
18. Wang X, Bishop CH. 2005. Improvement of ensemble reliability with a new dressing kernel. Q. J. R. Meteorol. Soc. 131: 965-986.
19. Wilks DS. 2006a. Comparison of ensemble-MOS methods in the Lorenz'96 setting. Meteorol. Appl. 13: 243-256.
20. Wilks DS. 2006b. Statistical methods in the atmospheric sciences. Academic Press: New York.