This research was supported through NCAR's U.S. Weather Research Program. Doug Nychka (NCAR/GSP) is thanked for his assistance with statistical issues. We thank Jean Thiebaux, Jim Purser, and Istvan Szunyogh of NCEP for their advice on an early version of this manuscript. Several library routines were borrowed from Numerical Recipes (Press et al. 1992).
This research was conducted partly while the first author was an Advanced Studies Program post-doctoral fellow at NCAR; we thank the NOAA-CIRES Climate Diagnostic Center for allowing us to finish this research.
Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev, 127 , 2741–2758.
Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev, 126 , 1719–1724.
Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res, 99 , . (C5),. 10143–10162.
Evensen, G., and P. J. van Leeuwen, 1996: Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model. Mon. Wea. Rev, 124 , 85–96.
Fisher, M., 1998: Development of a simplified Kalman filter. ECMWF Research Department Tech. Memo. 260, 16 pp. [Available from European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire, RG2 9AX, United Kingdom.].
Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc, 125 , 723–757.
Hamill, T. M., and C. M. Snyder, 2000: A hybrid ensemble Kalman filter–3D variational analysis scheme. Mon. Wea. Rev, 128 , 2905–2919.
Hamill, T. M., C. M. Snyder, and R. E. Morss, 2000: A comparison of probabilistic forecasts from bred, singular vector, and perturbed observation ensembles. Mon. Wea. Rev, 128 , 1835–1851.
Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev, 126 , 796–811.
Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev, 129 , 123–137.
Ide, K., P. Courtier, M. Ghil, and A. C. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential, and variational. J. Meteor. Soc. Japan, 75 , . (1B),. 181–189.
Keppenne, C. L., 2000: Data assimilation into a primitive equation model with a parallel ensemble Kalman filter. Mon. Wea. Rev, 128 , 1971–1981.
Le Dimet, F-X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A , 97–110.
Lermusiaux, P. F. J., and A. R. Robinson, 1999: Data assimilation via error subspace statistical estimation. Mon. Wea. Rev, 127 , 1385–1407.
Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: methodology and validation. Quart. J. Roy. Meteor. Soc, 122 , 73–119.
Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical interpolation system. Mon. Wea. Rev, 120 , 1747–1763.
Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in Fortran. 2d ed. Cambridge University Press, 963 pp.
Rabier, F., J-N. Thepaut, and P. Courtier, 1998: Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Quart. J. Roy. Meteor. Soc, 124 , 1–39.
Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc, 74 , 2317–2330.
van Leeuwen, P. J., 1999: Comment on “Data assimilation using an ensemble Kalman filter technique.”. Mon. Wea. Rev, 127 , 1374–1377.
Zou, X., A. Barcilon, I. M. Navon, J. Whitaker, and D. G. Cacuci, 1993: An adjoint sensitivity study of blocking in a two-layer isentropic model. Mon. Wea. Rev, 121 , 2833–2857.