50 citations as recorded by crossref.

1. Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter L. Nerger et al. https://doi.org/10.1007/s10236-006-0083-0
2. State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency G. De Lannoy et al. https://doi.org/10.1029/2006WR005100
3. Multiscale forecasting in the western North Atlantic: Sensitivity of model forecast skill to glider data assimilation A. Gangopadhyay et al. https://doi.org/10.1016/j.csr.2012.09.013
4. Constrained ensemble Kalman filter based on Kullback–Leibler divergence R. Li et al. https://doi.org/10.1016/j.jprocont.2019.05.011
5. Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter L. Nerger & W. Gregg https://doi.org/10.1016/j.jmarsys.2007.09.007
6. An Inflated Ensemble Filter for Ocean Data Assimilation with a Biased Coupled GCM S. Zhang & A. Rosati https://doi.org/10.1175/2010MWR3326.1
7. Systematic Bias in Land Surface Models G. Abramowitz et al. https://doi.org/10.1175/JHM628.1
8. The Potential Benefits of Handling Mixture Statistics via a Bi‐Gaussian EnKF: Tests With All‐Sky Satellite Infrared Radiances M. Chan et al. https://doi.org/10.1029/2022MS003357
9. Accounting for Model Errors in Ensemble Data Assimilation H. Li et al. https://doi.org/10.1175/2009MWR2766.1
10. Predictability of Two Types of El Niño Assessed Using an Extended Seasonal Prediction System by MIROC Y. Imada et al. https://doi.org/10.1175/MWR-D-15-0007.1
11. Estimating river bathymetry from data assimilation of synthetic SWOT measurements Y. Yoon et al. https://doi.org/10.1016/j.jhydrol.2012.07.028
12. Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter G. De Lannoy et al. https://doi.org/10.1029/2006WR005449
13. A comparison of the performance of the 3-D super-ensemble and an ensemble Kalman filter for short-range regional ocean prediction B. Mourre & J. Chiggiato https://doi.org/10.3402/tellusa.v66.21640
14. A Time-Averaged Covariance Method in the EnKF for Argo Data Assimilation Z. Deng et al. https://doi.org/10.1080/07055900.2012.719823
15. A homogeneous linear estimation method for system error in data assimilation W. Wu et al. https://doi.org/10.1007/s11802-013-1918-1
16. Correcting for Surface Pressure Background Bias in Ensemble-Based Analyses S. Baek et al. https://doi.org/10.1175/2008MWR2787.1
17. Correcting circulation biases in a lower-resolution global general circulation model with data assimilation M. Canter et al. https://doi.org/10.1007/s10236-016-1022-3
18. Data assimilation methods in the Earth sciences R. Reichle https://doi.org/10.1016/j.advwatres.2008.01.001
19. The ensemble Kalman filter for combined state and parameter estimation G. Evensen https://doi.org/10.1109/MCS.2009.932223
20. Observation bias correction with an ensemble Kalman filter E. Fertig et al. https://doi.org/10.1111/j.1600-0870.2008.00378.x
21. Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK filter L. Nerger & W. Gregg https://doi.org/10.1016/j.jmarsys.2006.11.009
22. 4-D-Var or ensemble Kalman filter? E. Kalnay et al. https://doi.org/10.1111/j.1600-0870.2007.00261.x
23. Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data G. El Serafy et al. https://doi.org/10.1007/s12601-011-0015-x
24. Ensemble perturbation smoother for optimizing tidal boundary conditions by assimilation of High-Frequency radar surface currents – application to the German Bight A. Barth et al. https://doi.org/10.5194/os-6-161-2010
25. Ensemble Data Assimilation with the NCEP Global Forecast System J. Whitaker et al. https://doi.org/10.1175/2007MWR2018.1
26. Filtering inertia-gravity waves from the initial conditions of the linear shallow water equations A. Barth et al. https://doi.org/10.1016/j.ocemod.2007.06.007
27. Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean Z. Deng et al. https://doi.org/10.1029/2011JC006942
28. Error Covariance Modeling in the GMAO Ocean Ensemble Kalman Filter C. Keppenne et al. https://doi.org/10.1175/2007MWR2243.1
29. Improved estimation of dissolved oxygen levels in commercial aquaculture using the ensemble Kalman filter T. Jacobsson et al. https://doi.org/10.1016/j.compag.2026.112143
30. A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System C. Draper et al. https://doi.org/10.1175/JHM-D-14-0087.1
31. An ensemble recentering Kalman filter with an application to Argo temperature data assimilation into the NASA GEOS-5 coupled model C. Keppenne https://doi.org/10.1016/j.ocemod.2014.03.001
32. Evolutionary Algorithm-Based Error Parameterization Methods for Data Assimilation Y. Bai & X. Li https://doi.org/10.1175/2011MWR3641.1
33. Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico G. Gopalakrishnan et al. https://doi.org/10.1002/qj.3493
34. Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System D. Fairbairn et al. https://doi.org/10.3390/rs16030493
35. A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. https://doi.org/10.5194/gmd-16-5949-2023
36. Comparison and Sensitivity of ODASI Ocean Analyses in the Tropical Pacific C. Sun et al. https://doi.org/10.1175/MWR3405.1
37. The impact of covariance localization on the performance of an ocean EnKF system assimilating glider data in the Ligurian Sea S. Falchetti & A. Alvarez https://doi.org/10.1016/j.jmarsys.2017.12.008
38. An Ensemble Ocean Data Assimilation System for Seasonal Prediction Y. Yin et al. https://doi.org/10.1175/2010MWR3419.1
39. Sound speed profiles in high spatiotemporal resolution using multigrid three-dimensional variational method: a coastal experiment off northern Shandong Peninsula G. Hou et al. https://doi.org/10.1007/s00343-022-1295-y
40. An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting H. Yan et al. https://doi.org/10.1002/bit.28272
41. Bred Vectors and Tropical Pacific Forecast Errors in the NASA Coupled General Circulation Model S. Yang et al. https://doi.org/10.1175/2007MWR2118.1
42. Inferred variables in data assimilation: quantifying sensitivity to inaccurate error statistics M. JUCKES & B. LAWRENCE https://doi.org/10.1111/j.1600-0870.2008.00376.x
43. Controllability, not chaos, key criterion for ocean state estimation G. Gebbie & T. Hsieh https://doi.org/10.5194/npg-24-351-2017
44. Data assimilation with the EnKF in a 1-D numerical model of a North Sea station S. Ponsar & P. Luyten https://doi.org/10.1007/s10236-009-0224-3
45. Estimating forecast model bias in coupled global and limited-area models M. Kretschmer et al. https://doi.org/10.3402/tellusa.v67.28040
46. An Ensemble Adjustment Kalman Filter for the CCSM4 Ocean Component A. Karspeck et al. https://doi.org/10.1175/JCLI-D-12-00402.1
47. Improving estimated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint B. Li et al. https://doi.org/10.5194/hess-16-105-2012
48. Obstacles to High-Dimensional Particle Filtering C. Snyder et al. https://doi.org/10.1175/2008MWR2529.1
49. Assessing the utility of frequency dependent nudging for reducing biases in biogeochemical models K. Lagman et al. https://doi.org/10.1016/j.ocemod.2014.06.006
50. Comparing Assimilation of Synthetic Soil Moisture Versus C‐Band Backscatter for Hyper‐Resolution Land Surface Modeling L. Sun et al. https://doi.org/10.1029/2020WR028921