A transformed lagged ensemble forecasting technique for increasing ensemble size

Monthly Weather Review

Volumn 135, Issue 4, 2007, Pages 1424-1438

  Lawrence, Andrew R ^a,b   Hansen, James A ^a,c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTS   (United Kingdom)

^c NAVAL RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDARY CONDITIONS; INTEGRATION; MATHEMATICAL MODELS; PERTURBATION TECHNIQUES; PROBABILITY; STATISTICS;

DATA ASSIMILATION; ENSEMBLE FORECASTS; NUMERICAL WEATHER PREDICTION MODELS; PERTURBATION INFLATION FACTOR;

WEATHER FORECASTING;

BOUNDARY CONDITION; CLIMATE PREDICTION; DATA ASSIMILATION; ENSEMBLE FORECASTING; NUMERICAL MODEL; PROBABILITY; STATISTICAL ANALYSIS;

EID: 34248376958     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/MWR3357.1     Document Type: Article

References (24)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
2. Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
3. Buizza, R., 1997: Potential forecast skill of ensemble prediction, and spread and skill distributions of the ECMWF ensemble prediction system. Mon. Wea. Rev., 125, 99-119.
4. Ebisuzaki, W., and E. Kalnay, 1991: Ensemble experiments with a new lagged average forecasting scheme. WMO research activities in atmospheric and oceanic modelling, WMO Rep. 15, Geneva, Switzerland, 6.31-6.32.
5. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143-10 162.
6. Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367.
7. Hansen, J. A., and L. A. Smith, 2000: The role of operational constraints in selecting supplementary observations. J. Atmos. Sci., 57, 2859-2871.
8. Hoffmann, R. N., and E. Kalnay, 1983: Lagged average forecasting, an alternative to Monte Carlo forecasting. Tellus, 35A, 100-118.
9. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
10. Jazwinski, A. H., 1969: Adaptive filtering. Automatica, 5, 475-485.
11. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, 341 pp.
12. Kharin, V., and F. W. Zwiers, 2003: Improved seasonal probability forecasts. J. Climate, 16, 1684-1701.
13. Lorenz, E. N., 1996: Predictability-a problem partly solved. Proc. Seminar on Predictability, Vol. 1, Reading, United Kingdom, ECMWF, 1-18.
14. Lorenz, E. N., and K. A. Emanuel, 1998: Optimal sites for supplementary weather observations: Simulations with a small model. J. Atmos. Sci., 55, 399-414.
15. Majumdar, S., C. Bishop, R. Buizza, and R. Gelaro, 2002: A comparison of ensemble transform Kalman filter targeting guidance with ECMWF and NRL total energy singular vector guidance. Quart. J. Roy. Meteor. Soc., 128, 1-24.
16. Palmer, T. N., F. Molteni, R. Mureau, R. Buizza, P. Chapelet, and J. Tribbia, 1992: Ensemble prediction. Proc. Seminar on Validation of Models over Europe, Reading, United Kingdom, ECMWF, 21-66.
17. Smith, L., and J. Hansen, 2004: Extending the limits of forecast verification with the minimum spanning tree. Mon. Wea. Rev., 132, 1522-1528.
18. Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square-root filters. Mon. Wea. Rev., 131, 1485-1490.
19. Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317-2330.
20. Wang, X., and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 1140-1158.
21. Wang, X., and S. J. Juliet, 2004: Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble? Mon. Wea. Rev., 132, 1590-1605.
22. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
23. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.
24. Wilks, D. S., 2002: Smoothing forecast ensembles with fitted probability distributions. Quart. J. Roy. Meteor. Soc., 128, 2821-2836.