Ensemble clustering in deterministic ensemble Kalman filters

References

• Amezcua, J. 2012. Advances in Sequential Data Assimilation and Numerical Weather Forecasting: An Ensemble Transform Kalman–Bucy Filter, a Study on Clustering in Deterministic Ensemble Square Root Filters, and a Test of a New Time Stepping Scheme in an Atmospheric Model. PhD Dissertation. University of Maryland, College Park.
   Google Scholar
• Anderson J. L. An ensemble adjustment filter for data assimilation. Mon. Weather Rev. 2001; 129: 2884–2903.
   Web of Science ®Google Scholar
• Anderson J. L. A non-Gaussian ensemble filter update for data assimilation. Mon. Weather Rev. 2010; 138: 4186–4198. 10.3402/tellusa.v64i0.18039.
   Web of Science ®Google Scholar
• Bishop C. H. Etherton B. J. Majumdar S. J. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Weather Rev. 2001; 129: 420–436.
   Web of Science ®Google Scholar
• Burgers G. van Leeuwen P. J. Evensen G. Analysis scheme in the ensemble Kalman filter. Mon. Weather Rev. 1998; 126: 1719–1724.
   Web of Science ®Google Scholar
• Evans, E, Bhatti, N, Kinney, J, Pann, L, Peña, M and co-authors. 2004. RISE undergraduates find that regime changes in Lorenz's model are predictable. Bull. Am. Meteorol. Soc. 85, 520–524. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Greybush S. J. Kalnay E. Miyoshi T. Ide K. Hunt B. R. Balance and ensemble Kalman filter localization techniques. Mon. Weather Rev. 2011; 139: 511–522. 10.3402/tellusa.v64i0.18039.
   Web of Science ®Google Scholar
• Houtekamer P. I. Mitchell H. I. Data assimilation using an ensemble Kalman filter technique. Mon. Weather Rev. 1998; 126: 796–811.
   Web of Science ®Google Scholar
• Hunt B. R. Kostelich E. J. Szunyogh I. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys. D. 2007; 230: 112–126. 10.3402/tellusa.v64i0.18039.
   Web of Science ®Google Scholar
• Kalman R. E. A new approach to linear filtering and prediction problems. 1960; 82: 35–45. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Kalnay E. Li H. Miyoshi T. Yang S. Ballabrera-Poy J. 4D-Var or ensemble Kalman filter?. Trans. ASME J. Basic Eng. D. 2007; 59: 758–773. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Kalnay E. Yang S. Accelerating the spin-up of ensemble Kalman filtering. Tellus A. 2010; 136B: 1644–1651. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Lawson W. G. Hansen J. A. Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth. Mon. Q. J. Roy. Meteorol. Soc. 2004; 132: 1966–1981.
   Google Scholar
• Leeuwenburgh O. Evensen G. Bertino L. The impact of ensemble filter definition on the assimilation of temperature profiles in the tropical Pacific. Weather Rev. 2005; 131: 3291–3300. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Livings D. M. Dance S. L. Nichols N. K. Unbiased ensemble square root filters. Q. J. Roy. Meteorol. Soc. 2008; 237: 1021–1028. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Lorenz E. N. Deterministic non-periodic flow. Phys. D. 1963; 20: 130–141.
   Google Scholar
• Lorenz, E. N. 1996. Predictability: A problem partly solved. Proc. Semina ron Predictability. vol. 1, ECMWF, Reading, Berkshire, UK. 1–18.
   Google Scholar
• Lorenz E. N. Emanuel K. A. Optimal sites for supplementary weather observations: simulations with a small model. 1998; 55: 399–414.
   Google Scholar
• Miller R. N. Ghil M. Gauthiez F. Advanced data assimilation in strongly nonlinear dynamical systems. J. Atmos. Sci. 1994; 51: 1037–1056.
   Web of Science ®Google Scholar
• Miyoshi, T. 2005. Ensemble Kalman Filter Experiments with a Primitive-equation Global Model. PhD Thesis, University of Maryland: College Park, 226. pp.
   Google Scholar
• Miyoshi T. The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. 2011; 139: 1519–1535. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Molteni F. Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments. Mon. Weather Rev. 2003; 20: 175–191.
   Google Scholar
• Ott, E, Hunt, B. R, Szunyogh, I, Zimin, A. V, Kostelich, E. J and co-authors. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Clim. Dyn. 56, 415–428. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Sakov P. Oke P. R. Implications of the form of the ensemble transformation in the ensemble square root filter. Tellus A. 2008; 136: 1042–1053. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Tippett M. K. Anderson J. L. Bishop C. H. Hamill T. M. Whitaker J. S. Ensemble square-root filters. Mon. Weather. Rev. 2003; 131: 1485–1490.
   Web of Science ®Google Scholar
• Toth Z. Kalnay E. Ensemble forecasting at NCEP: the breeding method. 1997; 125: 3297–3318.
   Google Scholar
• Wang X. Bishop C. H. Julier S. Which is better, and ensemble of positive–negative pairs or a centered spherical ensemble?. Mon. Weather Rev. 2004; 132: 1590–1605.
   Web of Science ®Google Scholar
• Whitaker J. S. Hamill T. M. Ensemble data assimilation without perturbed observations. Mon. Weather Rev. 2002; 130: 1913–1924.
   Web of Science ®Google Scholar
• Yang S. Kalnay E. Hunt B. R. Bowler N. E. Weight interpolation for efficient data assimilation with the local ensemble transform Kalman filter. Mon. Weather Rev. 2009; 135: 251–262. 10.3402/tellusa.v64i0.18039.
   Google Scholar
• Yang, S, Kalnay, E and Hunt, B. R. In press. Handling nonlinearity in Ensemble Kalman Filter: Experiments with the three-variable Lorenz model. Mon. Weather Rev.
   Google Scholar