4-D-Var or ensemble Kalman filter?

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
• Andersson, E., Fisher, M., Holm, E., Isaksen, L., Radnoti, G. and co-authors. 2005. Will the 4D-Var approach be defeated by nonlinearity? ECMWF Tech Memo 479. Available at: http://www.ecmwf.int/publications.
   Google Scholar
• Baek, S.-J., Hunt, B. R., Kalnay, E., Ott, E. and Szunyogh, I., 2006. Local ensemble Kalman filtering in the presence of model bias. Tellus 58, 293–306.
   Google Scholar
• Bengtsson, L. and Hodges, K. I., 2005. On the impact of humidity observations in numerical weather prediction. Tellus 57A, 701–708.
   Google Scholar
• Bishop, C. H., Etherton, B. J. and Majumdar, S. J., 2001. Adaptive sampling with ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Wea. Re v. 129, 420–436.
   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
• Cohn, S., Da Silva, A., Guo, J., Sienkiewicz, M. and Lamich, D., 1998. Assessing the effects of data selection with the DA0 physical-space statistical analysis system. Mon. Wea. Re v. 126, 2913–2926.
   Web of Science ®Google Scholar
• Corazza, M., Kalnay, E., Patil, D. J., Ott, E., Yorke, J. A. and co-authors. 2002. Use of the breeding technique in the estimation of the background error covariance matrix for a quasi-geostrophic model. Paper 6.4 in the ANIS Symposium on Observations, Data Assimilation and Probabilistic Prediction, Orlando, FA, January 14-17 2002. Available at: http://ams.confex.com/ams/pdfpapers/28755.pdf.
   Google Scholar
• Corazza, M., Kalnay, E., Patil, D. J., Yang, S.-C., Morss, R. and co-authors. 2003. Use of the breeding technique to estimate the structure of the analysis “error of the day”. Nonlinear Processes in Geophysics 10, 233-243.
   Google Scholar
• Corazza, M., Kalnay, E. and Yang, S.-C., 2007. An implementation of the local ensemble Kalman filter in a quasi-geostrophic model and comparison with 3D-Var. Nonlinear Proc. Phys. 14, 89–101.
   Web of Science ®Google Scholar
• Danforth, C. M., Kalnay, E. and Miyoshi, T. 2007. Estimating and correcting global weather model error. Mon. Wea. Re v. 134, 281–299.
   Google Scholar
• Davolio, S. and A. Buzzi. 2004. A nudging scheme for the assimilation of precipitation data into a mesoscale model. Weather and Forecasting 19 (5), 855–871.
   Web of Science ®Google Scholar
• Dee, D. P. and da Silva, A. M., 1998. Data assimilation in the presence of forecast bias. Quart. J. Raoy. Meteor Soc. 126, 269–295.
   Google Scholar
• Dee, D. P. and Todling, R., 2000. Data assimilation in the presence of forecast bias: the GEOS moisture analysis. Mon. Wea. Re v. 128, 3268–3282.
   Google Scholar
• De Pondeca, M. S. E V., Purser, R. J., Parrish, D. F. and Derber, J. C., 2006. Comparison of strategies for the specification of anisotropies in the covariances of a three-dimensional atmospheric data assimilation system, NOAA/NCEP Office Note 452, 13 pp. Available at: www.emc.ncep.noaa.gov/officenotes/newemotes/on452.pdf.
   Google Scholar
• Derber, J., Puser, R., Wu, W.-S., Treadon, R., Pondeca, M. and co-authors. 2003. Flow-dependent Thin grid-point 3D-Var. Available at: //www.ecmwf.int/publications/library/ecpublications/_pdf/seminar/2003/sem2003_derber.pdf
   Google Scholar
• Ehrendorfer, M. and Tribbia, J., 1997. Optimal prediction of forecast error covariances through singular vectors. J. Atmos. Sc i. 54, 286–313.
   Web of Science ®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(C5), 10 143-10 162.
   Google Scholar
• Evensen, G. and van Leewen, P. J., 1996. Assimilation of Geosat altimeter data for the Aghulas current using the ensemble Kalman Filter with a quasi-geostrophic model. Mon. Wea. Re v. 124, 85–96.
   Google Scholar
• Evensen, G., 1997. Advanced data assimilation for strongly nonlinear dynamics. Mon. Wea. Re v. 125, 1342–1354.
   Google Scholar
• Evensen, G., 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocen. Dyn. 53, 343–367.
   Google Scholar
• Fertig, E., Harlim, J. and Hunt, B., 2007a. A comparative study of 4D-Var and 4D ensemble Kalman filter: perfect model simulations with lorenz-96. Tellus 59, 96–101.
   Google Scholar
• Fertig, E., Hunt, B., Ott, E. and Szunyogh, I., 2007b. Assimilating nonlocal observations with a local ensemble Kalman Filter. Tellus, in press.
   Google Scholar
• Fisher, M. and Hollingsworth, A., 2004. Evaluation of reduced rank Kalman filters. Paper J1.10 of ANIS 16th Conference on Numerical Weather Prediction, 84th ANIS Annual Meeting, Seattle, Wash, January 12-15 2004. Available at: ams.confex.com/ams/84Annual/techprogram/paper_74522.htm
   Google Scholar
• Fisher, M., L, eutbecher, M. and Kelly, G., 2005. On the equivalence between Kalman smoothing and weak-constraint four-dimensional variational data assimilation. Q. J.R. Meteorol. Soc. 3235-3246.
   Google Scholar
• Gaspari, G. and Cohn, S. E., 1999. Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor Soc. 125,723–757.
   Web of Science ®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
• Houtekamer, P. L., Mitchell, H. L., Pellerin, G., Buehner, M., Charron, M. and co-authors. 2005. Atmospheric data assimilation with an ensemble Kalman filter: results with real observations. Mon. Wea. Rev. 133, 604-620.
   Google Scholar
• Houtekamer, P. L. and Mitchell, H. L., 2005. Ensemble Kalman filtering. Q. J. Roy. Met. Soc. 131, 3269–3290.
   Web of Science ®Google Scholar
• Hunt, B. R., 2005. An efficient implementation of the local ensemble Kalman filter. Available at: http://arxiv.org/abs/physics/0511236.
   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, I., 2007. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D, 230, 112–126.
   Web of Science ®Google Scholar
• Ide, K., Courtier, P., Ghil, M. and Lorenc, A., 1997. Unified notation for data assimilation: operational, sequential and variational. J. Meteor Soc. Jpn. 75, 181–189.
   Web of Science ®Google Scholar
• Järvinen, H., Andersson, E. and Bouttier, E, 1999. Variational assimilation of time sequences of surface observations with serially correlated errors. Tellus 51A, 469–488.
   Google Scholar
• Kalnay, E., 2003. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, Cambridge, 341 pp.
   Google Scholar
• Keppenne, C. and Rienecker, H., 2002. Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model. Mon. Wea. Re v. 130, 2951–2965.
   Google Scholar
• Keppenne, C., Rienecker, M., Kurkowski, N. and Adamec, D., 2005. Ensemble Kalman Filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction. Nonlinear Proc. Geophys. 12, 491–503.
   Web of Science ®Google Scholar
• Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G. and co-authors. 2001. The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull. Amer Meteor Soc. 82 (2), 247-267.
   Google Scholar
• Lawson, W. G. and Hansen, J. A., 2004. Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth. Mon. Wea. Re v. 132, 1966–1981.
   Google Scholar
• Li, Hong, Kalnay, E., Miyoshi, T. and Danforth, C., 2007. Ensemble Kalman Filter in the presence of model errors. Available at: http://ams.confex.com/ams/pdfpapers/120175.pdf
   Google Scholar
• Liu, J., Fertig, E., Li, H., Kalnay, E., Hunt, B. and co-authors. 2007. Comparison between local ensemble transform Kalman filter and PSAS in the NASA finite volume GCM: perfect model experiments. Available at: http://arxiv.org/ftp/physics/papers/0703/0703066.pdf.
   Google Scholar
• Lorenc, A. C., 2003. The potential of the ensemble Kalman filter for NWP - a comparison with 4D-Var. Quart. J. Roy. Meteor Soc. 129, 3183–3203.
   Web of Science ®Google Scholar
• Lorenz, E., 1963. Deterministic non-periodic flow. J. Atmos. Sc i. 20, 130–141.
   Web of Science ®Google Scholar
• Miller, R., Ghil, M. and Gauthiez, F., 1994. Advanced data assimilation in strongly nonlinear dynamical systems, J. Atmos. Sc i. 51, 1037–1056.
   Web of Science ®Google Scholar
• Miyoshi, T., 2005. Ensemble Kalman filter experiments with a Primitive-Equation global model. Doctoral dissertation, University of Maryland, College Park, 197 pp. Available at: https://drum. umd.edu/dspace/handle/1903/3046.
   Google Scholar
• Miyoshi, T. and Yamane, S., 2007. Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., in press.
   Google Scholar
• Molteni, E, 2003. Atmospheric simulations using a GCM with simplified physical parameterizations. I: model climatology and variability in multi-decadal experiments. Clim. Dyn. 20, 175–191.
   Web of Science ®Google Scholar
• Morss, R. E., Emanuel, K. A. and Snyder, C., 2001. Idealized adaptive observations strategies for improving numerical weather prediction. J. Atmos. Sc i. 58, 210–234.
   Web of Science ®Google Scholar
• Nerger, L., Hiller, W. and Scroeter, J., 2005. A comparison of error subspace Kalman filters. Tellus 57A, 715–735.
   Google Scholar
• Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J. and co-authors. 2002. Exploiting local low dimensionality of the atmospheric dynamics for efficient Kalman filtering. ArXiv :archive/paper 020358, Available at: http://arxiv.org/abs/physics/020358.
   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
• Parrish, D. E and Derber, J. C., 1992. The National Meteorological Center’s Spectral Statistical-Interpolation Analysis System. Mon. Wea. Re v. 120, 1747–1763.
   Web of Science ®Google Scholar
• Pires, C., Vautard, R. and Talagrand, O., 1996. On extending the limits of variational assimilation in chaotic systems. Tellus, 48A, 96–121.
   Google Scholar
• Rabier, E, Jarvinen, H., Klinker, E., Mahfouf, J. -E and Simmons, A., 2000. The ECMWF operational implementation of four-dimensional variational physics. Q. J. R. MeteoroL Soc., 126, 1143-1170.
   Google Scholar
• Rabier, E and Liu, Z., 2003. Variational assimilation: theory and overview. Available at: http://www.ecmwf.int/publications/library/ecpublications/_pdf/seminar/2003/sem2003_rabier.pdf
   Google Scholar
• Riishojgaard, L.-P, 1998. A direct way of specifying flow-dependent background error correlations for meteorological analysis systems. Tellus 50A, 42–57.
   Google Scholar
• Rotunno, R. and Bao, J. W., 1996. A case study of cyclogenesis using a model hierarchy. Mon. Wea. Re v. 124, 1051–1066.
   Web of Science ®Google Scholar
• Schröter, J., Seiler, U. and Wenzel, M., 1993. Variational assimilation of Geosat into an eddy-resolving model of the Gulf Stream extension area. J. Phys. Ocean. 23, 925–953.
   Web of Science ®Google Scholar
• Szunyogh, I., Kostelich, E. J., Gyarmati, G., Patil, D. J., Hunt, B. R. and co-authors. 2005. Assessing a local ensemble Kalman filter: Perfect model experiments with the NCEP global model. Tellus 57A, 528-545.
   Google Scholar
• Szunyogh, I., Kostelich, E., Gyarmati, G., Kalnay, E., Hunt, B. R. and co-authors. 2007. Assessing a local ensemble Kalman filter: assimilating real observations with the NCEP global model. Under revision in Tellus.
   Google Scholar
• Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M. and Whitaker, J. S., 2003. Ensemble square root filters. Mon. Wea. Re v. 131, 1485–1490.
   Web of Science ®Google Scholar
• Tremolet, Yannick, 2005. Accounting for an imperfect model in 4D-Var. ECMWF Tech Memo #477. Available at: www.ecmwf.it/publications.
   Google Scholar
• Wang, X. and Bishop, C. H., 2003. A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sc i. 60, 1140–1158.
   Web of Science ®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
• Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y. and Toth, Z., 2007. Ensemble Data Assimilation with the NCEP Global Forecast System.Mon. Wea. Rev., under revision. Available at http://www.cdc.moaa.gov/people/jeffrey.s.whitaker/Manuscripts/pubs.html
   Google Scholar
• Yang, S-C., Corazza, M., Carrassi, A. and Kalnay, E., 2007. Comparison of ensemble-based and variational-based data assimilation schemes in a quasi-geostrophic model. AMS 10th Symposium on In-tegrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface. Available at: http://ams.confex.com/ams/pdfpapers/101581.pdf
   Google Scholar
• Yang, S-C., Baker, D., Li, H., Huff, M., Nagpal, G. and co-authors. 2006. Data assimilation as synchronization of truth and model: experiments with the 3-variable Lorenz system. J. Atmos. Sci. 63, 2340-2354.
   Google Scholar
• Zupanski, M., 2005. Maximum likelihood ensemble filter: theoretical aspects. Mon. Wea. Re v. 133, 1710–1726.
   Web of Science ®Google Scholar