We thank Stephen Cohn and Richard Ménard for suggesting the Schur product with a compactly supported correlation function in place of the cutoff radius that we had employed in HM. We thank Luc Fillion, whose constructive criticism of the HM algorithm provided the impetus for this study. We express our appreciation to Michel Valin for introducing us to MPI and for suggestions that improved the performance of the parallel algorithm. We thank Stéphane Laroche and Monique Tanguay for their helpful internal reviews of the manuscript. The comments of the two anonymous reviewers helped us clarify a number of points and sharpen the focus of the paper.
Anderson, B. D. O., and J. B. Moore, 1979: Optimal Filtering. Prentice-Hall, 357 pp.
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.
Andersson, E., J. Pailleux, J.-N. Thépaut, J. R. Eyre, A. P. McNally, G. A. Kelly, and P. Courtier, 1994: Use of cloud-cleared radiances in three/four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc.,120, 627–653.
Brown, R. G., 1983: Introduction to Random Signal Analysis and Kalman Filtering. Wiley and Sons, 347 pp.
Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev.,126, 1719–1724.
Cohn, S. E., and D. F. Parrish, 1991: The behavior of forecast error covariances for a Kalman filter in two dimensions. Mon. Wea. Rev.,119, 1757–1785.
——, A. da Silva, J. Guo, M. Sienkiewicz, and D. Lamich, 1998: Assessing the effects of data selection with the DAO physical-space statistical analysis system. Mon. Wea. Rev.,126, 2913–2926.
Côté, J., J.-G. Desmarais, S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998a: The operational CMC–MRB Global Environmental Multiscale (GEM) model. Part II: Results. Mon. Wea. Rev.,126, 1397–1418.
——, S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998b: The operational CMC–MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev.,126, 1373–1395.
Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev.,126, 2287–2299.
——, D. F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Wea. Forecasting,6, 538–547.
Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res.,99 (C5), 10 143–10 162.
——, 1997: Advanced data assimilation for strongly nonlinear dynamics. Mon. Wea. Rev.,125, 1342–1354.
——, 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.
Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc.,125, 723–757.
Gropp, W., E. Lusk, and A. Skjellum, 1994: Using MPI. Portable Parallel Programming with the Message-Passing Interface. Scientific and Engineering Computation Series, The MIT Press, 307 pp.
Horn, R. A., and C. R. Johnson, 1985: Matrix Analysis. Cambridge University Press, 561 pp.
Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev.,126, 796–811.
——, and ——, 1999: Reply. Mon. Wea. Rev.,127, 1378–1379.
Jiang, S., and M. Ghil, 1993: Dynamical properties of error statistics in a shallow-water model. J. Phys. Oceanogr.,23, 2541–2566.
Keppenne, C. L., 2000: Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. Mon. Wea. Rev.,128, 1971–1981.
Lyster, P. M., S. E. Cohn, R. Ménard, L.-P. Chang, S.-J. Lin, and R. G. Olsen, 1997: Parallel implementation of a Kalman filter for constituent data assimilation. Mon. Wea. Rev.,125, 1674–1686.
McNally, A. P., J. C. Derber, W. Wu, and B. B. Katz, 2000: The use of TOVS level-1b radiances in the NCEP SSI analysis system. Quart. J. Roy. Meteor. Soc.,126, 689–724.
Miller, R. N., E. F. Carter Jr., and S. T. Blue, 1999: Data assimilation into nonlinear stochastic models. Tellus,51A, 167–194.
Mitchell, H. L., and P. L. Houtekamer, 2000: An adaptive ensemble Kalman filter. Mon. Wea. Rev.,128, 416–433.
Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in FORTRAN. The Art of Scientific Computing. 2d ed. Cambridge University Press, 963 pp.
Rocken, C., and Coauthors, 1997: Analysis and validation of GPS/MET data in the neutral atmosphere. J. Geophys. Res.,102 (D25), 29 849–29 866.
Snir, M., S. W. Otto, S. Huss-Lederman, D. W. Walker, and J. Dongarra, 1996: MPI. The Complete Reference. Scientific and Engineering Computation Series, The MIT Press, 336 pp.
Steinle, P. J., B. R. Harris, and R. S. Seaman, 1999: Variational data assimilation at the Bureau of Meteorology. Preprints, 13th Conf. on Numerical Weather Prediction, Denver, CO, Amer. Meteor. Soc., 33–36.
Stobie, J., 2000: An efficient objective analysis system for parallel computers. Proc. 2d Int. Conf. on Reanalyses, Reading, United Kingdom, World Climate Research Programme, WMO/TD No. 985, 103–106.
Thomas, S. J., M. Desgagné, and M. Valin, 2000: High-resolution weather forecasting: A teraflop sustained on RISC/cache or vector processors. High Performance Computing Systems and Applications, A. Pollard, D. J. K. Mewhort, and D. F. Weaver, Eds., Kluwer Academic, 289–299.
van Leeuwen, P. J., 1999: Comments on “Data assimilation using an ensemble Kalman filter technique.” Mon. Wea. Rev.,127, 1374–1377.