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Volumn 143, Issue 6, 2015, Pages 2028-2042

Two-stage filtering for joint state-parameter estimation

Author keywords

Bayesian methods; Filtering techniques; Kalman filters

Indexed keywords

BAYESIAN NETWORKS; CLIMATE MODELS; KALMAN FILTERS; PARAMETERIZATION; SLIP FORMING; UNCERTAINTY ANALYSIS;

EID: 84943396957     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-14-00176.1     Document Type: Article
Times cited : (34)

References (31)
  • 1
    • 33750616040 scopus 로고    scopus 로고
    • Ensemblebased simultaneous state and parameter estimation withMM5
    • Aksoy, A., F. Zhang, and J. W. Nielsen-Gammon, 2006: Ensemblebased simultaneous state and parameter estimation withMM5. Geophys. Res. Lett., 33, L12801, doi:10.1029/2006GL026186.
    • (2006) Geophys. Res. Lett , vol.33
    • Aksoy, A.1    Zhang, F.2    Nielsen-Gammon, J.W.3
  • 2
    • 17144401880 scopus 로고    scopus 로고
    • Parameter estimation in an atmospheric GCM using the ensemble Kalman filter
    • Annan, J. D., D. J. Lunt, J. C. Hargreaves, and P. J. Valdes, 2005: Parameter estimation in an atmospheric GCM using the ensemble Kalman filter. Nonlinear Processes Geophys., 12, 363-371, doi:10.5194/npg-12-363-2005.
    • (2005) Nonlinear Processes Geophys , vol.12 , pp. 363-371
    • Annan, J.D.1    Lunt, D.J.2    Hargreaves, J.C.3    Valdes, P.J.4
  • 3
    • 0036475447 scopus 로고    scopus 로고
    • A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
    • Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp, 2002:A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process., 50, 174-188, doi:10.1109/78.978374.
    • (2002) IEEE Trans. Signal Process , vol.50 , pp. 174-188
    • Arulampalam, M.S.1    Maskell, S.2    Gordon, N.3    Clapp, T.4
  • 4
    • 33645575318 scopus 로고    scopus 로고
    • Local ensemble Kalman filtering in the presence of model bias
    • Baek, S.-J., B. R. Hunt, E. Kalnay, E. Ott, and I. Szunyogh, 2006: Local ensemble Kalman filtering in the presence of model bias. Tellus, 58A, 293-306, doi:10.1111/j.1600-0870.2006.00178.x.
    • (2006) Tellus , vol.58 A , pp. 293-306
    • Baek, S.-J.1    Hunt, B.R.2    Kalnay, E.3    Ott, E.4    Szunyogh, I.5
  • 5
    • 84901617672 scopus 로고    scopus 로고
    • Nonglobal parameter estimation using local ensemble Kalman filtering
    • Bellsky, T., J. Berwald, and L. Mitchell, 2014: Nonglobal parameter estimation using local ensemble Kalman filtering. Mon. Wea. Rev., 142, 2150-2164, doi:10.1175/MWR-D-13-00200.1.
    • (2014) Mon. Wea. Rev , vol.142 , pp. 2150-2164
    • Bellsky, T.1    Berwald, J.2    Mitchell, L.3
  • 6
    • 0032439201 scopus 로고    scopus 로고
    • Analysis scheme in the ensemble Kalman filter
    • Burgers, G., J. P. Van Leeuwen, andG. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724, doi:10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.
    • (1998) Mon. Wea. Rev , vol.126 , pp. 1719-1724
    • Burgers, G.1    Van Leeuwen, J.P.2    Evensen, G.3
  • 7
    • 79952524167 scopus 로고    scopus 로고
    • Parameter estimation using a particle method: Inference mixing coefficients from sealevel observations
    • Carrassi, A., and S. Vannitsem, 2011: Parameter estimation using a particle method: Inference mixing coefficients from sealevel observations. Quart. J. Roy. Meteor. Soc., 137, 435-451, doi:10.1002/qj.762.
    • (2011) Quart. J. Roy. Meteor. Soc , vol.137 , pp. 435-451
    • Carrassi, A.1    Vannitsem, S.2
  • 8
    • 0002205556 scopus 로고    scopus 로고
    • Rao-Blackwellization of sampling schemes
    • Casella, G., and C. Robert, 1996: Rao-Blackwellization of sampling schemes. Biometrika, 83, 81-94, doi:10.1093/biomet/83.1.81.
    • (1996) Biometrika , vol.83 , pp. 81-94
    • Casella, G.1    Robert, C.2
  • 9
    • 77955465991 scopus 로고    scopus 로고
    • State and parameter estimation in stochastic dynamical models
    • DelSole, T., and X. Yang, 2010: State and parameter estimation in stochastic dynamical models. Physica D, 239, 1781-1788, doi:10.1016/j.physd.2010.06.001.
    • (2010) Physica D , vol.239 , pp. 1781-1788
    • DelSole, T.1    Yang, X.2
  • 11
    • 0001460136 scopus 로고    scopus 로고
    • On sequential Monte Carlo sampling methods for Bayesian filtering
    • Doucet, A., S. Godsill, and C. Andrieu, 2000: On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput., 10, 197-208, doi:10.1023/A:1008935410038.
    • (2000) Stat. Comput , vol.10 , pp. 197-208
    • Doucet, A.1    Godsill, S.2    Andrieu, C.3
  • 12
    • 0028193070 scopus 로고
    • Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics
    • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143-10 162, doi:10.1029/94JC00572.
    • (1994) J. Geophys. Res , vol.99 , pp. 10143-10162
    • Evensen, G.1
  • 13
    • 0014552637 scopus 로고
    • Treatment of bias in recursive filtering
    • Friedland, B., 1969: Treatment of bias in recursive filtering. IEEE Trans. Auto. Control, 14, 359-367, doi:10.1109/TAC.1969.1099223.
    • (1969) IEEE Trans. Auto. Control , vol.14 , pp. 359-367
    • Friedland, B.1
  • 14
    • 33846679917 scopus 로고    scopus 로고
    • Model error estimation in ensemble data assimilation
    • Gillijns, S., and B. De Moor, 2007: Model error estimation in ensemble data assimilation. Nonlinear Processes Geophys., 14, 59-71.
    • (2007) Nonlinear Processes Geophys , vol.14 , pp. 59-71
    • Gillijns, S.1    De Moor, B.2
  • 15
    • 0027580559 scopus 로고
    • Novel approach to nonlinear/non-Gaussian Bayesian state estimation
    • Gordon, N. J., D. J. Salmond, and A. F. M. Smith, 1993: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar Signal Process., 140, 107-113.
    • (1993) Radar Signal Process , vol.140 , pp. 107-113
    • Gordon, N.J.1    Salmond, D.J.2    Smith, A.F.M.3
  • 16
    • 0032024819 scopus 로고    scopus 로고
    • Data assimilation using an ensemble Kalman filter technique
    • Houtekamer, P., and H. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.
    • (1998) Mon. Wea. Rev , vol.126 , pp. 796-811
    • Houtekamer, P.1    Mitchell, H.2
  • 17
    • 79957462609 scopus 로고    scopus 로고
    • "Variable localization" in an ensemble Kalman filter: Application to the carbon cycle data assimilation
    • Kang, J. S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide, 2011: "Variable localization" in an ensemble Kalman filter: Application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673.
    • (2011) J. Geophys. Res , vol.116
    • Kang, J.S.1    Kalnay, E.2    Liu, J.3    Fung, I.4    Miyoshi, T.5    Ide, K.6
  • 18
    • 77958565843 scopus 로고    scopus 로고
    • Reducing forecast errors due to model imperfections using ensemble Kalman filtering
    • Koyama, H., and M. Watanabe, 2010: Reducing forecast errors due to model imperfections using ensemble Kalman filtering. Mon. Wea. Rev., 138, 3316-3332, doi:10.1175/2010MWR3067.1.
    • (2010) Mon. Wea. Rev , vol.138 , pp. 3316-3332
    • Koyama, H.1    Watanabe, M.2
  • 19
    • 0032359151 scopus 로고    scopus 로고
    • Sequential Monte Carlo methods for dynamic systems
    • Liu, J. S., and R. Chen, 1998: Sequential Monte Carlo methods for dynamic systems. J. Amer. Stat. Assoc., 93 (443), 1032-1044.
    • (1998) J. Amer. Stat. Assoc , vol.93 , Issue.443 , pp. 1032-1044
    • Liu, J.S.1    Chen, R.2
  • 20
    • 84927120004 scopus 로고    scopus 로고
    • Predictability: A problem partly solved
    • T. Palmer andR. Hagedorn, Eds., Cambridge University Press
    • Lorenz, E., 2006: Predictability: A problem partly solved. Predictability ofWeather and Climate, T. Palmer andR. Hagedorn, Eds., Cambridge University Press, 40-58.
    • (2006) Predictability ofWeather and Climate , pp. 40-58
    • Lorenz, E.1
  • 21
    • 11944268965 scopus 로고    scopus 로고
    • Dual state-parameter estimation of hydrological models using ensemble Kalman filter
    • Moradkhani, H., H. Sorooshian, H. Gupta, and P. Houser, 2005: Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour., 28, 135-147, doi:10.1016/j.advwatres.2004.09.002.
    • (2005) Adv. Water Resour , vol.28 , pp. 135-147
    • Moradkhani, H.1    Sorooshian, H.2    Gupta, H.3    Houser, P.4
  • 22
    • 34547192187 scopus 로고    scopus 로고
    • Merging particle filter for sequential data assimilation
    • Nakano, S., G. Ueno, and T. Higuchi, 2007: Merging particle filter for sequential data assimilation. Nonlinear Processes Geophys., 14, 395-408, doi:10.5194/npg-14-395-2007.
    • (2007) Nonlinear Processes Geophys , vol.14 , pp. 395-408
    • Nakano, S.1    Ueno, G.2    Higuchi, T.3
  • 23
    • 8844258829 scopus 로고    scopus 로고
    • A local ensemble Kalman filter for atmospheric data assimilation
    • Ott, E., and et al., 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428, doi:10.1111/j.1600-0870.2004.00076.x.
    • (2004) Tellus , vol.56 A , pp. 415-428
    • Ott, E.1
  • 24
    • 23844494086 scopus 로고    scopus 로고
    • Marginalized particle filters formixed linear/nonlinear state-space models
    • Schön, T., F. Gustafsson, and P. Nordlund, 2005:Marginalized particle filters formixed linear/nonlinear state-space models. IEEE Trans. Signal Process., 53, 2279-2289, doi:10.1109/TSP.2005.849151.
    • (2005) IEEE Trans. Signal Process , vol.53 , pp. 2279-2289
    • Schön, T.1    Gustafsson, F.2    Nordlund, P.3
  • 25
    • 64149083162 scopus 로고    scopus 로고
    • Obstacles to high-dimensional particle filtering
    • Snyder, C., T. Bengtsson, P. Bickel, and J. Anderson, 2008: Obstacles to high-dimensional particle filtering. Mon. Wea. Rev., 136, 4629-4640, doi:10.1175/2008MWR2529.1.
    • (2008) Mon. Wea. Rev , vol.136 , pp. 4629-4640
    • Snyder, C.1    Bengtsson, T.2    Bickel, P.3    Anderson, J.4
  • 26
    • 34848876373 scopus 로고    scopus 로고
    • Sequential state and variance estimation within the ensemble Kalman filter
    • Stroud, J., and T. Bengtsson, 2007: Sequential state and variance estimation within the ensemble Kalman filter. Mon. Wea. Rev., 135, 3194-3208, doi:10.1175/MWR3460.1.
    • (2007) Mon. Wea. Rev , vol.135 , pp. 3194-3208
    • Stroud, J.1    Bengtsson, T.2
  • 27
    • 34047242150 scopus 로고    scopus 로고
    • Parameter estimation using a particle method: Inference mixing coefficients from sea-level observations
    • Vossepoel, F. C., and P. J. Van Leeuwen, 2007: Parameter estimation using a particle method: Inference mixing coefficients from sea-level observations. Mon. Wea. Rev., 135, 1006-1020, doi:10.1175/MWR3328.1.
    • (2007) Mon. Wea. Rev , vol.135 , pp. 1006-1020
    • Vossepoel, F.C.1    Van Leeuwen, P.J.2
  • 28
    • 0001225908 scopus 로고    scopus 로고
    • Combined parameter and state estimation in simulation-based filtering
    • A. Doucet et al., Eds., Springer
    • West, M., and J. Liu, 2001: Combined parameter and state estimation in simulation-based filtering. Sequential Monte Carlo Methods in Practices, A. Doucet et al., Eds., Springer, 197-223.
    • (2001) Sequential Monte Carlo Methods in Practices , pp. 197-223
    • West, M.1    Liu, J.2
  • 29
    • 0032409329 scopus 로고    scopus 로고
    • Probabilistic hindcasts and projections of the coupled climate, carbon cycle and Atlantic meridional overturning circulation system: A Bayesian fusion of century-scale observations with a simple model
    • Wikle, C. K., L. M. Berliner, and N. Cressie, 1998: Probabilistic hindcasts and projections of the coupled climate, carbon cycle and Atlantic meridional overturning circulation system: A Bayesian fusion of century-scale observations with a simple model. Environ. Ecol. Stat., 5, 117-154, doi:10.1023/A:1009662704779.
    • (1998) Environ. Ecol. Stat , vol.5 , pp. 117-154
    • Wikle, C.K.1    Berliner, L.M.2    Cressie, N.3
  • 30
    • 20544463416 scopus 로고    scopus 로고
    • Effects of stochastic parametrizations in the Lorenz '96 system
    • Wilks, D., 2005: Effects of stochastic parametrizations in the Lorenz '96 system. Quart. J. Roy. Meteor. Soc., 131, 389-407, doi:10.1256/qj.04.03.
    • (2005) Quart. J. Roy. Meteor. Soc , vol.131 , pp. 389-407
    • Wilks, D.1
  • 31
    • 77249111719 scopus 로고    scopus 로고
    • Using the ensemble Kalman filter to estimate multiplicative model parameters
    • Yang, X., and T. DelSole, 2009: Using the ensemble Kalman filter to estimate multiplicative model parameters. Tellus, 61A, 601-609, doi:10.1111/j.1600-0870.2009.00407.x.
    • (2009) Tellus , vol.61 A , pp. 601-609
    • Yang, X.1    DelSole, T.2


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