Articles | Volume 10, issue 3
https://doi.org/10.5194/npg-10-253-2003
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the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Special issue:
https://doi.org/10.5194/npg-10-253-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Sequential parameter estimation for stochastic systems
G. A. Kivman
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
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- Sigma-Point Kalman Filter Data Assimilation Methods for Strongly Nonlinear Systems J. Ambadan & Y. Tang 10.1175/2008JAS2681.1
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- Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach J. Rings et al. 10.5194/hess-14-545-2010
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- Sampling-free linear Bayesian updating of model state and parameters using a square root approach O. Pajonk et al. 10.1016/j.cageo.2012.05.017
- Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review J. RUIZ et al. 10.2151/jmsj.2013-201
- The estimation of time-invariant parameters of noisy nonlinear oscillatory systems M. Khalil et al. 10.1016/j.jsv.2014.10.002
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- Performance assessment, diagnosis, and optimal selection of non-linear state filters A. Tulsyan et al. 10.1016/j.jprocont.2013.10.015
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- Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties B. Khodabandeloo et al. 10.3390/s17112656
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- Particle Filtering in Geophysical Systems P. van Leeuwen 10.1175/2009MWR2835.1
- Parameter estimation using chaotic time series J. Annan 10.3402/tellusa.v57i5.14735
- Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model A. Subramanian et al. 10.1175/JAS-D-11-0332.1
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- Model Error Estimation Employing an Ensemble Data Assimilation Approach D. Zupanski & M. Zupanski 10.1175/MWR3125.1
- Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models A. Weerts & G. El Serafy 10.1029/2005WR004093
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- Bayesian system ID: optimal management of parameter, model, and measurement uncertainty N. Galioto & A. Gorodetsky 10.1007/s11071-020-05925-8
- Estimating parameters in stochastic systems: A variational Bayesian approach M. Vrettas et al. 10.1016/j.physd.2011.08.013
- Nonlinear filters for chaotic oscillatory systems M. Khalil et al. 10.1007/s11071-008-9349-z
- A new method for parameter estimation in nonlinear dynamical equations L. Wang et al. 10.1007/s00704-014-1113-3
- A Weak-Constraint-Based Data Assimilation Scheme for Estimating Surface Turbulent Fluxes J. Qin et al. 10.1109/LGRS.2007.904004
- A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography I. Hoteit et al. 10.1175/2007MWR1927.1
- Sampling free iterative PCE filter for state and parameter estimation of nonlinear dynamical systems W. van Dijk et al. 10.1016/j.jcp.2024.113118
- Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments P. Brasseur et al. 10.1256/qj.05.142
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- Data assimilation with the weighted ensemble Kalman filter N. Papadakis et al. 10.1111/j.1600-0870.2010.00461.x
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