59 citations as recorded by crossref.

1. Sigma-Point Kalman Filter Data Assimilation Methods for Strongly Nonlinear Systems J. Ambadan & Y. Tang https://doi.org/10.1175/2008JAS2681.1
2. An iterative ensemble Kalman filter with one-step-ahead smoothing for state-parameters estimation of contaminant transport models M. Gharamti et al. https://doi.org/10.1016/j.jhydrol.2015.05.004
3. Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC A. Siripatana et al. https://doi.org/10.1007/s10236-017-1074-z
4. Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach J. Rings et al. https://doi.org/10.5194/hess-14-545-2010
5. Parameter Estimation Using a Particle Method: Inferring Mixing Coefficients from Sea Level Observations F. Vossepoel & P. Jan van Leeuwen https://doi.org/10.1175/MWR3328.1
6. A new method based on remote sensing observations and data assimilation for estimation of evapotranspiration over field crops J. Qin et al. https://doi.org/10.1080/00288230709510378
7. State and Parameter Estimation with an SIR Particle Filter in a Three-Dimensional Groundwater Pollutant Transport Model S. Chang et al. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000584
8. Sampling-free linear Bayesian updating of model state and parameters using a square root approach O. Pajonk et al. https://doi.org/10.1016/j.cageo.2012.05.017
9. Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review J. RUIZ et al. https://doi.org/10.2151/jmsj.2013-201
10. The estimation of time-invariant parameters of noisy nonlinear oscillatory systems M. Khalil et al. https://doi.org/10.1016/j.jsv.2014.10.002
11. Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts Z. Shen et al. https://doi.org/10.5194/gmd-17-1651-2024
12. Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model B. Liu et al. https://doi.org/10.1016/j.jhydrol.2016.01.048
13. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering B. Liu et al. https://doi.org/10.1175/MWR-D-14-00292.1
14. Performance assessment, diagnosis, and optimal selection of non-linear state filters A. Tulsyan et al. https://doi.org/10.1016/j.jprocont.2013.10.015
15. Sigma-point particle filter for parameter estimation in a multiplicative noise environment J. Ambadan & Y. Tang https://doi.org/10.1029/2011MS000065
16. Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties B. Khodabandeloo et al. https://doi.org/10.3390/s17112656
17. Implicit Estimation of Ecological Model Parameters B. Weir et al. https://doi.org/10.1007/s11538-012-9801-6
18. A simple numerical method based simultaneous stochastic perturbation for estimation of high dimensional matrices H. Hoang & R. Baraille https://doi.org/10.1007/s11045-018-0551-y
19. Particle Filtering in Geophysical Systems P. van Leeuwen https://doi.org/10.1175/2009MWR2835.1
20. Parameter estimation using chaotic time series J. Annan https://doi.org/10.3402/tellusa.v57i5.14735
21. Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model A. Subramanian et al. https://doi.org/10.1175/JAS-D-11-0332.1
22. An improved method for nonlinear parameter estimation: a case study of the Rössler model W. He et al. https://doi.org/10.1007/s00704-015-1528-5
23. Parameter estimation using chaotic time series J. Annan https://doi.org/10.3402/tellusa.v57i5.14735
24. Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China M. Chen et al. https://doi.org/10.3390/w10111612
25. An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters B. Ait-El-Fquih & I. Hoteit https://doi.org/10.1175/MWR-D-16-0485.1
26. A Note on the Particle Filter with Posterior Gaussian Resampling X. Xiong et al. https://doi.org/10.1111/j.1600-0870.2006.00185.x
27. Model Error Estimation Employing an Ensemble Data Assimilation Approach D. Zupanski & M. Zupanski https://doi.org/10.1175/MWR3125.1
28. Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models A. Weerts & G. El Serafy https://doi.org/10.1029/2005WR004093
29. Parameter variations in prediction skill optimization at ECMWF P. Ollinaho et al. https://doi.org/10.5194/npg-20-1001-2013
30. A two-stage inflation method in parameter estimation to compensate for constant parameter evolution in Community Earth System Model Z. Shen & Y. Tang https://doi.org/10.1007/s13131-021-1856-5
31. Up the ante on bioeconomic submodels of marine food webs: A data assimilation-based approach N. Ekerhovd & S. Kvamsdal https://doi.org/10.1016/j.ecolecon.2016.09.005
32. On closure parameter estimation in chaotic systems J. Hakkarainen et al. https://doi.org/10.5194/npg-19-127-2012
33. A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter B. Ait‐El‐Fquih & I. Hoteit https://doi.org/10.1002/qj.3716
34. Tracking noisy limit cycle oscillation with nonlinear filters M. Khalil et al. https://doi.org/10.1016/j.jsv.2009.09.009
35. Online tuning of ocean biogeochemical model parameters using ensemble estimation techniques: Application to a one-dimensional model in the North Atlantic M. Gharamti et al. https://doi.org/10.1016/j.jmarsys.2016.12.003
36. Adaptive Filter for High Dimensional Inverse Engineering Problems: From Theory to Practical Implementation H. Hoang & R. Barailles https://doi.org/10.4236/eng.2013.55A010
37. Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example L. Tuppi et al. https://doi.org/10.5194/gmd-13-5799-2020
38. Sequential weak constraint parameter estimation in an ecosystem model S. Losa et al. https://doi.org/10.1016/j.jmarsys.2003.06.001
39. Bayesian system ID: optimal management of parameter, model, and measurement uncertainty N. Galioto & A. Gorodetsky https://doi.org/10.1007/s11071-020-05925-8
40. Estimating parameters in stochastic systems: A variational Bayesian approach M. Vrettas et al. https://doi.org/10.1016/j.physd.2011.08.013
41. Nonlinear filters for chaotic oscillatory systems M. Khalil et al. https://doi.org/10.1007/s11071-008-9349-z
42. A new method for parameter estimation in nonlinear dynamical equations L. Wang et al. https://doi.org/10.1007/s00704-014-1113-3
43. A Weak-Constraint-Based Data Assimilation Scheme for Estimating Surface Turbulent Fluxes J. Qin et al. https://doi.org/10.1109/LGRS.2007.904004
44. A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography I. Hoteit et al. https://doi.org/10.1175/2007MWR1927.1
45. Sampling free iterative PCE filter for state and parameter estimation of nonlinear dynamical systems W. van Dijk et al. https://doi.org/10.1016/j.jcp.2024.113118
46. Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments P. Brasseur et al. https://doi.org/10.1256/qj.05.142
47. Ocean state estimation from hydrography and velocity observations during EIFEX with a regional biogeochemical ocean circulation model M. Losch et al. https://doi.org/10.1016/j.jmarsys.2013.09.003
48. Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments M. Tong & M. Xue https://doi.org/10.1175/2007MWR2071.1
49. Parameter identification of river current and diffusion by reduced Kalman filter finite element method O. Kanai & M. Kawahara https://doi.org/10.1002/fld.2694
50. Using the Kalman filter for parameter estimation in biogeochemical models C. Trudinger et al. https://doi.org/10.1002/env.910
51. THE ENSEMBLE KALMAN FILTER FOR MULTIDIMENSIONAL BIOECONOMIC MODELS S. KVAMSDAL & L. SANDAL https://doi.org/10.1111/nrm.12070
52. A variational Bayesian approach for ensemble filtering of stochastically parametrized systems B. Ait‐El‐Fquih et al. https://doi.org/10.1002/qj.4481
53. Prediction error sampling procedure based on dominant Schur decomposition. Application to state estimation in high dimensional oceanic model H. Hoang & R. Baraille https://doi.org/10.1016/j.amc.2011.09.012
54. Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. https://doi.org/10.1109/JAS.2023.123537
55. Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model E. Simon & L. Bertino https://doi.org/10.1016/j.jmarsys.2011.07.007
56. Data assimilation with the weighted ensemble Kalman filter N. Papadakis et al. https://doi.org/10.1111/j.1600-0870.2010.00461.x
57. Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data S. Losa et al. https://doi.org/10.1016/j.jmarsys.2012.07.008
58. Ensemble Kalman filter inference of spatially-varying Manning’s n coefficients in the coastal ocean A. Siripatana et al. https://doi.org/10.1016/j.jhydrol.2018.05.021
59. Parameter and state estimation with ensemble Kalman filter based algorithms for convective‐scale applications Y. Ruckstuhl & T. Janjić https://doi.org/10.1002/qj.3257