-
1
-
-
33750616040
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
10
-
-
0002165558
-
Rao-Blackwellised particle filtering for dynamic Bayesian networks
-
Morgan Kaufmann Publishers
-
Doucet, A., N. de Freitas, K. Murphy, and S. J. Russell, 2000: Rao-Blackwellised particle filtering for dynamic Bayesian networks. Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, 176-183.
-
(2000)
Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
, pp. 176-183
-
-
Doucet, A.1
de Freitas, N.2
Murphy, K.3
Russell, S.J.4
-
11
-
-
0001460136
-
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
-
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
-
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
-
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
-
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
-
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
-
"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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
|