Corresponding author address: Dr. Thomas M. Hamill, NOAA/Earth System Research Laboratory/Physical Sciences Division, R/PSD1, 325 Broadway, Boulder, CO 80305. Email: tom.hamill@noaa.gov
Ambadan, J. T., and Y. Tang, 2009: Sigma-point Kalman filter data assimilation methods for strongly nonlinear systems. J. Atmos. Sci., 66 , 261–285.
Anderson, J. L., 2006: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230 , 99–111.
Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A , 72–83.
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.
Bishop, C. H., and D. Hodyss, 2009a: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models. Tellus, 61A , 84–96.
Bishop, C. H., and D. Hodyss, 2009b: Ensemble covariances adaptively localized with ECO-RAP. Part 2: A strategy for the atmosphere. Tellus, 61A , 97–111.
Buehner, M., and M. Charron, 2007: Spectral and spatial localization of background-error correlations for data assimilation. Quart. J. Roy. Meteor. Soc., 133 , 615–630.
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 quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 , 10143–10162.
Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53 , 343–367.
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., 2006: Ensemble-based atmospheric data assimilation. Predictability of Weather and Climate, T. N. Palmer and R. Hagedorn, Eds., Cambridge University Press, 124–156.
Hamill, T. M., and J. S. Whitaker, 2005: Accounting for the error due to unresolved scales in ensemble data assimilation: A comparison of different approaches. Mon. Wea. Rev., 133 , 3132–3147.
Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background-error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129 , 2776–2790.
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.
Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133 , 604–620.
Houtekamer, P. L., H. L. Mitchell, and X. Deng, 2009: Model error representation in an operational ensemble Kalman filter. Mon. Wea. Rev., 137 , 2126–2143.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230 , 112–126.
Kepert, J., 2009: Covariance localization and balance in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135 , 1157–1176.
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.
Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20 , 130–141.
Lorenz, E. N., 1996: Predictability: A problem partly solved. Proc. Seminar on Predictability, Vol. 1, ECMWF, 1–18. [Available from ECMWF Library, Shinfield Park, Reading, Berkshire RG2 9AX, United Kingdom].
Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130 , 2791–2808.
Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130 , 1913–1924. Corrigendum, 134, 1722.
Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136 , 463–482.
Zhang, F., C. Snyder, and J. Sun, 2004: Impact of initial estimate and observation availability on convective-scale data assimilation with an ensemble kalman filter. Mon. Wea. Rev., 132 , 1238–1253.
Zhou, Y., D. McLaughlin, D. Entekhabi, and G-H. C. Ng, 2008: An ensemble multiscale filter for large nonlinear data assimilation problems. Mon. Wea. Rev., 136 , 678–698.
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Corresponding author address: Dr. Thomas M. Hamill, NOAA/Earth System Research Laboratory/Physical Sciences Division, R/PSD1, 325 Broadway, Boulder, CO 80305. Email: tom.hamill@noaa.gov
Corresponding author address: Dr. Thomas M. Hamill, NOAA/Earth System Research Laboratory/Physical Sciences Division, R/PSD1, 325 Broadway, Boulder, CO 80305. Email: tom.hamill@noaa.gov
Ambadan, J. T., and Y. Tang, 2009: Sigma-point Kalman filter data assimilation methods for strongly nonlinear systems. J. Atmos. Sci., 66 , 261–285.
Anderson, J. L., 2006: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230 , 99–111.
Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A , 72–83.
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.
Bishop, C. H., and D. Hodyss, 2009a: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models. Tellus, 61A , 84–96.
Bishop, C. H., and D. Hodyss, 2009b: Ensemble covariances adaptively localized with ECO-RAP. Part 2: A strategy for the atmosphere. Tellus, 61A , 97–111.
Buehner, M., and M. Charron, 2007: Spectral and spatial localization of background-error correlations for data assimilation. Quart. J. Roy. Meteor. Soc., 133 , 615–630.
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 quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 , 10143–10162.
Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53 , 343–367.
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., 2006: Ensemble-based atmospheric data assimilation. Predictability of Weather and Climate, T. N. Palmer and R. Hagedorn, Eds., Cambridge University Press, 124–156.
Hamill, T. M., and J. S. Whitaker, 2005: Accounting for the error due to unresolved scales in ensemble data assimilation: A comparison of different approaches. Mon. Wea. Rev., 133 , 3132–3147.
Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background-error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129 , 2776–2790.
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.
Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133 , 604–620.
Houtekamer, P. L., H. L. Mitchell, and X. Deng, 2009: Model error representation in an operational ensemble Kalman filter. Mon. Wea. Rev., 137 , 2126–2143.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230 , 112–126.
Kepert, J., 2009: Covariance localization and balance in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135 , 1157–1176.
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.
Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20 , 130–141.
Lorenz, E. N., 1996: Predictability: A problem partly solved. Proc. Seminar on Predictability, Vol. 1, ECMWF, 1–18. [Available from ECMWF Library, Shinfield Park, Reading, Berkshire RG2 9AX, United Kingdom].
Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130 , 2791–2808.
Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130 , 1913–1924. Corrigendum, 134, 1722.
Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136 , 463–482.
Zhang, F., C. Snyder, and J. Sun, 2004: Impact of initial estimate and observation availability on convective-scale data assimilation with an ensemble kalman filter. Mon. Wea. Rev., 132 , 1238–1253.
Zhou, Y., D. McLaughlin, D. Entekhabi, and G-H. C. Ng, 2008: An ensemble multiscale filter for large nonlinear data assimilation problems. Mon. Wea. Rev., 136 , 678–698.
All Time | Past Year | Past 30 Days | |
---|---|---|---|
Abstract Views | 0 | 0 | 0 |
Full Text Views | 783 | 527 | 204 |
PDF Downloads | 127 | 45 | 4 |