Four-dimensional local ensemble transform Kalman filter: numerical experiments with a global circulation model

References

• Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Re v. 129, 2884–2903.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Bishop, C. H., Etherton, B. J. and Majumdar, S. J. 2001. Adaptive sampling with the ensemble transform Kalman filter part I: the theoretical aspects. Mon. Wea. Re v. 129, 420–436.
   Web of Science ®Google Scholar
• Buehner, M. 2005. Ensemble-derived stationary and flow-dependent background error covariances: evaluation in a quasi-operational NVVP setting. Quart. J. Roy. Met. Soc. 131, 1013–1043.
   Web of Science ®Google Scholar
• Burgers, G., van Leeuwen, P. J. and Evensen, G. 1998. On the analysis scheme in the ensemble Kalman filter. Mon. Wea. Re v. 126, 1719–1724.
   Web of Science ®Google Scholar
• Courtier, P., Thepaut, J.-N. and Hollingsworth, A. 1994. A strategy for operational implementation of 4D-VAR, using an incremental approach. Quart. J. Roy. Met. Soc. 120, 1367–1387.
   Web of Science ®Google Scholar
• Courtier, P., Andersson, E., Heckley, W., Pailleux, J., Vasiljevic, D. and co-authors. 1998. The ECMWF implementation of three-dimensional variational assimilation. Quart. J. Roy. Met. Soc. 124, 1783–1807.
   Web of Science ®Google Scholar
• Le Dimet, F.-X. and Talagrand, O. 1986. Variational algorithm for analysis and assimilation of meteorological observations: theoretical aspects. Tellus 38A, 97–110.
   Google Scholar
• 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.
   Google Scholar
• Fertig, E. J., Harlim, J. and Hunt, B. R. 2007. A comparative study of 4D-VAR and a four-dimensional ensemble Kalman filter: perfect model simulations with Lorenz-96. Tellus 59A, 96–100.
   Google Scholar
• Fisher, M. and Courtier, P. 1995. Estimating the covariance matrix of analysis and forecast error in variational data assimilation. ECMWF Tech. Memo. 220, 5pp.
   Google Scholar
• Gustafsson, N., Berre, L., Hörnquist, S., Huang, X.-Y., Lindskog, M. and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model. Part I: general formulation and the background error constraint. Tellus 53A, 425-446.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Harlim, J., 2006. Errors in the initial conditions for numerical weather prediction: a study of error growth patterns and error reduction with ensemble filtering, PhD dissertation, University of Maryland.
   Google Scholar
• Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Re v. 126, 796–811.
   Web of Science ®Google Scholar
• Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Re v. 129, 123–137.
   Web of Science ®Google Scholar
• Huang, X.-Y., Morgensen, K. S., and Yang, X. 2002. First-guess at the appropriate time: the HIRLAM implementation and experiments. In Proceedings, HIRLAM workshop on variational data assimilation and remote sensing, Finland,28-43.
   Google Scholar
• Hunt, B. R., Kalnay, E., Kostelich, E. J., Ott, E., Patil, D. J. and co-authors. 2004. Four-dimensional ensemble Kalman filtering. Tellus 56A, 273-277.
   Google Scholar
• Hunt, B. R., Kostelich, E. J. and Szunyogh, J. 2007. Efficient data assimilation for a spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D, 230, 112-126.
   Google Scholar
• Keppenne, C. 2000. Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. Mon. Wea. Re v. 128, 1971–1981.
   Web of Science ®Google Scholar
• Lindskog, M., Gustafsson, N., Navascues, B., Mogensen, K. S., Huang, X.-Y. and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model. Part II: observation handling and data assimilation experiments. Tellus 53A, 447-468.
   Google Scholar
• Lorene, A. C. 2003. The potential of the ensemble Kalman filter for NWP - a comparison with 4D-VAR. Quart. J. Roy. Met. Soc. 129, 3183–3203.
   Web of Science ®Google Scholar
• Lorenz, E .N., 1996. Predictability—a problem partly solved. In: Proceedings on Predictability, held at ECMWF on 4-8 September 1995, 1–18.
   Google Scholar
• Molteni, F. 2003. Atmospheric simulations using a GCM with simplified physical parametrizations, I: model climatology and variability in multi-decadal experiments. Clim. Dyn. 20, 175–191.
   Web of Science ®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. Tellus 56A, 415-428.
   Google Scholar
• Rabier, F., Thepaut, J.-N. and Courtier, P. 1998. Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Quart. J. Roy. Met. Soc. 124, 1–39.
   Web of Science ®Google Scholar
• Rabier, F., Järvinen, H., Mahfouf, J. -E and Simmons, A. 2000. The ECMWF operational implementation of four-dimensional variational assimilation: experimental results with simplified physics. Quart. J. Roy. Met. Soc. 126, 1143-1170.
   Google Scholar
• Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M. and Whitaker, J. S. 2003. Ensemble square-roots filters. Mon. Wea. Re v. 131, 1485–1490.
   Web of Science ®Google Scholar
• Wang, X., Bishop, C. H. and Julier, S. J. 2004. Which Is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble?. Mon. Wea. Re v. 132, 1590–1605.
   Web of Science ®Google Scholar
• Wang, X., Snyder, C. and Hamill, T. M. 2007. On the theoretical equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes. Mon. Wea. Re v. 135, 222–227.
   Google Scholar
• Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Re v. 130, 1913–1924.
   Web of Science ®Google Scholar
• Zupanslci, M. 2005. Maximum likelihood ensemble filter: theoretical aspects. Mon. Wea. Re v. 133, 1710–1726.
   Google Scholar