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Volumn 61, Issue 5, 2009, Pages 601-609

Using the ensemble Kalman filter to estimate multiplicative model parameters

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; DATA ASSIMILATION; DYNAMIC PROPERTY; ENSEMBLE FORECASTING; ESTIMATION METHOD; KALMAN FILTER; WEATHER FORECASTING;

EID: 77249111719     PISSN: 02806495     EISSN: 16000870     Source Type: Journal    
DOI: 10.1111/j.1600-0870.2009.00407.x     Document Type: Article
Times cited : (46)

References (21)
  • 1
    • 0035655814 scopus 로고    scopus 로고
    • An ensemble adjustment filter for data assimilation
    • Anderson, J. L. 2001. An ensemble adjustment filter for data assimilation. Mon. Wea. Rev. 129, 2884-2903.
    • (2001) Mon. Wea. Rev. , vol.129 , pp. 2884-2903
    • Anderson, J.L.1
  • 2
    • 34247642674 scopus 로고    scopus 로고
    • An adaptive covariance inflation error correction algorithm for ensemble filters
    • Anderson, J. L. 2007. An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus 59A, 210-224.
    • (2007) Tellus , vol.59 A , pp. 210-224
    • Anderson, J.L.1
  • 3
    • 0033500692 scopus 로고    scopus 로고
    • A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts
    • 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. Rev. 127, 2741-2758.
    • (1999) Mon. Wea. Rev. , vol.127 , pp. 2741-2758
    • Anderson, J.L.1    Anderson, S.L.2
  • 4
    • 34548625197 scopus 로고    scopus 로고
    • Scalable implementations of ensemble filter algorithms for data assimilation
    • Anderson, J. L. and Collins, N. 2007. Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol. 24, 1452-1463.
    • (2007) J. Atmos. Oceanic Technol. , vol.24 , pp. 1452-1463
    • Anderson, J.L.1    Collins, N.2
  • 5
    • 33645575318 scopus 로고    scopus 로고
    • Local ensemble Kalman filtering in the presence of model bias
    • Baek, S.-J., Hunt, B. R., Kalney, E., Ott, E. and Szunyogh, I. 2006. Local ensemble Kalman filtering in the presence of model bias. Tellus 58A, 293-306.
    • (2006) Tellus , vol.58 A , pp. 293-306
    • Baek, S.-J.1    Hunt, B.R.2    Kalney, E.3    Ott, E.4    Szunyogh, I.5
  • 6
    • 0035270690 scopus 로고    scopus 로고
    • Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects
    • Bishop, C. H., Etherton, B. andMajumdar, S. J. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Wea. Rev. 129, 420-436.
    • (2001) Mon. Wea. Rev. , vol.129 , pp. 420-436
    • Bishop, C.H.1    Etherton, B.2    Majumdar, S.J.3
  • 7
    • 0000674568 scopus 로고    scopus 로고
    • Data assimilation in the presence of forecast bias
    • Dee, D. P. and da Silva, A. M. 1998. Data assimilation in the presence of forecast bias. Quart. J. R. Met. Soc. 124, 269-297.
    • (1998) Quart. J. R. Met. Soc. , vol.124 , pp. 269-297
    • Dee, D.P.1    da Silva, A.M.2
  • 8
    • 0034277367 scopus 로고    scopus 로고
    • Data assimilation in the presence of forecast bias: the GEOS moisture analysis
    • 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.
    • (2000) Mon. Wea. Rev. , vol.128 , pp. 3268-3282
    • Dee, D.P.1    Todling, R.2
  • 9
    • 77249134517 scopus 로고    scopus 로고
    • Stochastic parameter estimation with the ensemble Kalman filter
    • submitted
    • DelSole, T. and Yang, X. 2009. Stochastic parameter estimation with the ensemble Kalman filter. Physica D, submitted.
    • (2009) Physica D
    • DelSole, T.1    Yang, X.2
  • 10
    • 0028193070 scopus 로고
    • Sequential data assimilation with a nonlinear quasigeostrophicmodel using Monte Carlo methods to forecast error statistics
    • Evensen, G. 1994. Sequential data assimilation with a nonlinear quasigeostrophicmodel using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, 1043-1062.
    • (1994) J. Geophys. Res. , vol.99 , pp. 1043-1062
    • Evensen, G.1
  • 11
    • 0014552637 scopus 로고
    • Treatment of bias in resursive filtering
    • Friedland, B. 1969. Treatment of bias in resursive filtering. IEEE Trans. Autom. Control 14, 359-367.
    • (1969) IEEE Trans. Autom. Control , vol.14 , pp. 359-367
    • Friedland, B.1
  • 12
    • 0033023511 scopus 로고    scopus 로고
    • Construction of correlation functions in two and three dimensions
    • Gaspari, G. and Cohn, S. E. 1999. Construction of correlation functions in two and three dimensions. Quart. J. R. Met. Soc. 125, 723-757.
    • (1999) Quart. J. R. Met. Soc. , vol.125 , pp. 723-757
    • Gaspari, G.1    Cohn, S.E.2
  • 13
    • 33846679917 scopus 로고    scopus 로고
    • Model error estimation in ensemble data assimilation
    • Gillijns, S. and De Moor, B. 2007. Model error estimation in ensemble data assimilation. Nonlin. Process. Geophys. 14, 59-71.
    • (2007) Nonlin. Process. Geophys. , vol.14 , pp. 59-71
    • Gillijns, S.1    De Moor, B.2
  • 14
    • 0035506430 scopus 로고    scopus 로고
    • Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter
    • 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.
    • (2001) Mon. Wea. Rev. , vol.129 , pp. 2776-2790
    • Hamill, T.M.1    Whitaker, J.S.2    Snyder, C.3
  • 15
    • 0035129828 scopus 로고    scopus 로고
    • A sequential ensemble Kalman filter for atmospheric data assimilation
    • Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123-137.
    • (2001) Mon. Wea. Rev. , vol.129 , pp. 123-137
    • Houtekamer, P.L.1    Mitchell, H.L.2
  • 16
    • 0000241853 scopus 로고
    • Deterministic nonperiodic flow
    • Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci 20, 130-141.
    • (1963) J. Atmos. Sci. , vol.20 , pp. 130-141
    • Lorenz, E.N.1
  • 17
    • 0032004313 scopus 로고    scopus 로고
    • Optimal sites for supplementary weather observations: simulation with a small model
    • 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.
    • (1998) J. Atmos. Sci. , vol.55 , pp. 399-414
    • Lorenz, E.N.1    Emanuel, K.A.2
  • 19
    • 0036646009 scopus 로고    scopus 로고
    • Ensemble data assimilationwithout perturbed observations
    • Whitaker, J. and Hamill, T. M. 2002. Ensemble data assimilationwithout perturbed observations. Mon. Wea. Rev. 130, 1913-1924.
    • (2002) Mon. Wea. Rev. , vol.130 , pp. 1913-1924
    • Whitaker, J.1    Hamill, T.M.2
  • 20
    • 44449095043 scopus 로고    scopus 로고
    • Ensemble data assimilation with the NCEP global forecast system
    • Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y. and Toth, Z. 2008. Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Rev. 136, 463-482.
    • (2008) Mon. Wea. Rev. , vol.136 , pp. 463-482
    • Whitaker, J.S.1    Hamill, T.M.2    Wei, X.3    Song, Y.4    Toth, Z.5
  • 21
    • 33644881232 scopus 로고    scopus 로고
    • Model error estimation employing an ensemble data assimilation approach
    • Zupanski, D. and Zupanski,M. 2006. Model error estimation employing an ensemble data assimilation approach. Mon. Wea. Rev. 134, 1337-1354.
    • (2006) Mon. Wea. Rev. , vol.134 , pp. 1337-1354
    • Zupanski, D.1    Zupanski, M.2


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