References
- Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilation and forecasts. Mon. Wea. Re v. 127, 2741–2758.
- Bishop, C. H., Etherton, B. J. and Majumdar, S. J. 2001. Adaptive sam-pling with the ensemble transform Kalman filter. Mon. Wea. Re v. 129, 420–436.
- Bürger, G. and Cane, M. A. 1994. Interactive Kalman filtering. J. Geo-phys. Res. Oceans. 99, 8015–8031.
- Cohn, S. E. 1997. An introduction to estimation theory. J. Met. Soc. Japan 75, 257–288.
- Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statis-tics. J. Geophys. Res. 99, 10 143-10 162.
- Evensen, G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367.
- Evensen, G. 2004. Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn. 54, 539–560.
- Kivman, G. A. 2003. Sequential parameter estimation for stochastic sys-tems. Non. Process Geo. 10, 253–259.
- van Leeuwen, P. J. 2003. A variance-minimizing filter for large-scale applications. Mon. Wea. Re v. 131, 2071–2084.
- Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sc i. 20, 130–141.
- Silverman, B. W. 1986. Density estimation for statistics and data analysis. Chapman and Hall, 175pp.
- Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M. and Whitaker, J. S. 2003. Notes and Correspondence: Ensemble Square Root Filters. Mon. Wea. Re v. 131, 1485–1490.
- Whitaker, J. S. and Hamill, T. M. 2002. Ensemble Data Assimila-tion without Perturbed Observations. Mon. Wea. Re v. 130, 1913–1924.
- Zupanski, M. 2005. Maximum Likelihood Ensemble Filter: Theoretical Aspects. Mon. Wea. Re v. 133, 1710–1726.