Spatially and temporally varying adaptive covariance inflation for ensemble filters

References

• Anderson, J. L. 1996. A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Clim. 9, 1518–1530.
   Web of Science ®Google Scholar
• Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev. 129, 2894–2903.
   Web of Science ®Google Scholar
• Anderson, J. L. 2003. A local least squares framework for ensemble filtering. Mon. Wea. Rev. 131, 634–642.
   Web of Science ®Google Scholar
• Anderson, J. L. 2007a. An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus 59A, 210–224.
   Google Scholar
• Anderson, J. L. 2007b. Exploring the need for localization in ensemble data assimilation using an hierarchical ensemble filter. Physica D 230, 99–111.
   Web of Science ®Google Scholar
• Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementa-tion of the nonlinear filtering problem to produce ensemble assimila-tions and forecasts. Mon. Wea. Rev. 127, 2741–2758.
   Web of Science ®Google Scholar
• Anderson, J. L. and Collins, N. 2007. Scalable implementations of en-semble filter algorithms for data assimilation. J. Atmos. Ocean Tech. 24A, 1452–1463.
   Google Scholar
• Buizza, R., Miller, M. and Palmer, T. N. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system.Quart. J. Roy. MeteoroL Soc. 125, 2887-2908.
   Google Scholar
• Burgers, G., van Leeuwen, P. J. and Evensen, G. 1998. Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev. 126, 1719–1724.
   Web of Science ®Google Scholar
• Daley, R. 1993. Estimating the observation error statistics for atmo-spheric data assimilation. Ann. Geophys. 11, 634–647.
   Web of Science ®Google Scholar
• Dee, D. P. 1995. On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev. 123, 1128–1145.
   Web of Science ®Google Scholar
• Dee, D. P. and da Silva, A. M. 1999. Maximum-likelihood estimation of forecast and observation error covariance parameters, Part I: method-ology. Mon. Wea. Rev. 127, 1822–1834.
   Web of Science ®Google Scholar
• Dee, D. P. and Todling, R. 2000. Data assimilation in the presence of forecast bias: the GEOS moisture analysis. Mon. Wea. Rev. 128, 3268–3282.
   Web of Science ®Google Scholar
• Dee, D. R, Gaspari, G., Redder, C. Rulchovets, L. and da Silva, A. M. 1999. Maximum-likelihood estimation of forecast and observation error covariance parameters, Part II: applications. Mon. Wea. Rev. 127, 1835–1849.
   Web of Science ®Google Scholar
• Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to do forecast error statistics. J. Geophys. Res. 99(C5), 10143–10162.
   Web of Science ®Google Scholar
• Evensen, G., 2006. Data Assimilation: The Ensemble Kalman Filter. Springer,280 pp.
   Google Scholar
• Eyre, J. R., Kelly, G. A., McNally, A. P., Andersson, E. and Persson, A. 1993. Assimilation of TOVS radiance information through one-dimensional variational analysis.Quart. J. Roy. Meteor Soc. 119, 1427-1463.
   Google Scholar
• Furrer, R. and Bengtsson, T. 2007. Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. J. Multi-variate Anal. 98(2), 227–255.
   Web of Science ®Google Scholar
• Gaspari, G. and Cohn, S. E. 1999. Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteorol. Soc. 125, 723–757.
   Web of Science ®Google Scholar
• Hamill, T. M. and Whitaker, J. S. 2005. Accounting for error due to unresolved scales in ensemble data assimilation: a comparison of different approaches. Mon. Wea. Rev. 133, 3132–3147.
   Web of Science ®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. Rev. 129, 2776–2790.
   Web of Science ®Google Scholar
• Hansen, J. A. 2002. Accounting for model error in ensemble-based state estimation and forecasting. Mon. Wea. Rev. 130, 2373–2391.
   Web of Science ®Google Scholar
• Harlim, J. and Hunt, B. R. 2007. A non-gaussian ensemble filter for assimilating infrequent noisy observations. Tellus 59A, 225–237.
   Google Scholar
• Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123–137.
   Web of Science ®Google Scholar
• Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C. and Ballabrera-Poy, J. 2007. 4-D-Var or ensemble Kalman filter? Tellus 59A, 758-773.
   Google Scholar
• Keppenne, C. L. and Rienecker, M. M. 2002. Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model. Mon. Wea. Rev. 130, 2951–2965.
   Web of Science ®Google Scholar
• Kistler, R., Collins, W., Saha, S., White, G. and Woolen, J. 2001. The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and doc-umentation. Bull. Amer. Met. Soc. 82, 247-267.
   Google Scholar
• Lorenz, E. N. 1996. Predictability: a problem partly solved. In: Proc-ceedings of the ECMWF Seminar on Predictability, Vol. I, ECMWF, Reading, United Kingdom, 1-18.
   Google Scholar
• Lorenz, E. N. and Emanuel, K. A. 1998. Optimal sites for supplementary weather observations: simulation with a small model. J. Atmos. Sci. 55, 399–414.
   Web of Science ®Google Scholar
• Mitchell, H. L. and Houtekamer, P. L. 2000. An adaptive ensemble Kalman filter.Mon. Wea. Rev. 128, 416-433.
   Google Scholar
• Mitchell, H. L., Houtekamer, P. L. and Pellerin, G. 2002. Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev. 130, 2791–2808.
   Web of Science ®Google Scholar
• Ott, E., Hunt, B.,Szunyogh, I., Zimin, A., Kostelich, E. and co-authors 2004. A local ensemble Kalman filter for atmospheric data assimila-tion.Tellus 56A, 415-428.
   Google Scholar
• Pham, D. T. 2001. Stochastic methods for sequential data assimilation in strongly non-linear systems. Mon. Wea. Rev. 129, 1194–1207.
   Web of Science ®Google Scholar
• Sandu, A., Constantinescu, E. M., Carmichael, G. R., Chai, T., Seinfeld, J. H., and co-authors 2007. Localized ensemble Kalman filter dynamic data assimilation for atmospheric chemistry.Lecture Notes Comput. Sci. 4487, 1018-1025.
   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. Rev. 131, 1485-1490.
   Google Scholar
• Uppala, S. M., Kallber, P. W., Simmons, A. J., Andrae, U., Da Costa Bechtold, V. and 45 contributing authors, 2005. The ERA-40 re-analysis. Quart. J. Roy. Meteor Soc. 131, 2961-3012.
   Google Scholar
• Uzunoglu, B., Fletcher, S. J., Zupanslci, M. and Navon, I. M. 2007. Adaptive ensemble reduction and inflation.Quart. J. Roy. Meteor Soc. 133, 1281-1294.
   Google Scholar
• Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. 130, 1913–1924.
   Web of Science ®Google Scholar