References
- Anderson, J. L. 1996. A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate 9, 1518–1530.
- Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Re v. 129, 2884–2903.
- Anderson, J. L. 2003. A local least squares framework for ensemble filtering. Mon. Wea. Re v. 131, 634–642.
- Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Re v. 127, 2741–2758.
- Asselin, R. 1972. Frequency filter for time integrations. Mon. Wea. Re v. 100, 487–490.
- Bishop, C. H., Etherton, B. J. and Majumdar, S. 2001. Adaptive sampling with the ensemble transform Kalman filter, part I. Mon. Wea. Re v. 129, 420–436.
- Burgers, G., van Leeuwen, P. J. and Evensen, G. 1998. Analysis scheme in the ensemble Kalman filter. Mon. Wea. Re v. 126, 1719–1724.
- Durran, D. R. 1999. Numerical Methods for Wave Equations in Geophysical Fluid Dynamics. Springer, New York, 465 pp.
- 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.
- Fukumori, I. 2002. A partitioned Kalman filter and smoother. Mon. Wea. Re v. 130, 1370–1383.
- Haltiner, G. J. and Williams, R. T. 1980. Numerical Prediction and Dynamic Meteorology 2nd edn. Wiley, New York, 477 pp.
- Hamill, T. M., Whitaker, J. S. and Snyder, C. 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Re v. 129, 2776–2790.
- Hollingsworth, A. and Lönnberg, P. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus 38A, 111–136.
- Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Re v. 126, 796–811.
- Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Re v. 129, 123–137.
- Ide, K., Courtier, P., Ghil, M. and Lornec, A. 1997. Unified nontation for data assimilation. Operational sequential and variational. J. Meteor Soc. Japan 75, 181–189.
- Jazwinski, A. H. 1970. Stochastic Processes and Filtering Theory. Academic Press, New York, 376 pp.
- Kalnay, E. 2002. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, Cambridge, 341 pp.
- Keppenne, C. L. 2000. Data assimilation into a primitive equation model with a parallel ensemble Kalman filter. Mon. Wea. Re v. 128, 1971–1981.
- Le Dimet, F. X. and Talagrand, O. 1986. Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus 38A, 97–110.
- Liu, D. C. and Nocedal, J. 1989. On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503–528.
- Lorenz, E. N. 1963. Deterministic non-periodic flow. J. Atmos. Sc i. 20, 130–141.
- Miller, R. N., Ghil, M. and Gauthiez, P. 1994. Advanced data assimilation in strongly nonlinear dynamical system. J. Atmos. Sc i. 51, 1037–1056.
- Miller, R. N., Carter, E. F. and Blue, S. T. 1999. Data assimilation into nonlinear stochastic models. Tellus 51A, 167–194.
- Mitchell, H. L. and Houtekamer, P. L. 2000. An adaptive ensemble Kalman filter. Mon. Wea. Re v. 128, 416–433.
- Navon, I. M., Zou, X., Derber, J. and Sela, J. 1992. Variational data assimilation with an adiabatic version of the NMC spectral model. Mon. Wea. Re v. 120, 1433–1446.
- Pires, C., Vautard, R. and Talagrand, O. 1996. On extending the limits of variational assimilation in nonlinear chaotic systems. Tellus 48A, 96–121.
- Robert, A. 1969. The integration of a spectral model of the atmosphere by the implicit method.Proc. WMO/IUGG Symposium on IVW P. Japan Meteorological Society, Tokyo, Japan, 19-24.
- Saltzman, B. 1962. Finite amplitude free convection as an initial value problem - I. J. Atmos. Sc i. 19, 329–341.
- Sirkes, Z. and Tziperman, E. 1997. Finite difference of adjoint or adjoint of finite difference? Mon. Wea. Re v. 120, 3373–3378.
- Thiebaux, H. J. 1985. On approximations to geopotential and wind-field correlation structures. Tellus 37A, 126–131.
- Van Leeuwen, P. J. 1999. Comment on “Data assimilation using an ensemble Kalman filter technique”. Mon. Wea. Rev. 127, 1374–1377.
- Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Re v. 130, 1913–1924.
- Zhang, S. and Anderson, J. L. 2003. Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus 55A, 126–147.
- Zhang, S., Thou, X. and Ahlquist, J. E. 2001. Examination of numerical results from tangent linear and adjoint of discontinuous nonlinear models. Mon. Wea. Re v. 129, 2791–2804.
- Zupanslci, M. 1993. A preconditioning algorithm for large-scale minimization problems. Tellus 45A, 478–492.