16 citations as recorded by crossref.

1. Ensemble Kalman Filters for State Estimation and Prediction of Two-Time Scale Nonlinear Systems With Application to Gas Turbine Engines N. Daroogheh et al. https://doi.org/10.1109/TCST.2018.2870044
2. Using the ensemble Kalman filter to estimate multiplicative model parameters X. Yang & T. Delsole https://doi.org/10.1111/j.1600-0870.2009.00407.x
3. Two-Stage Filtering for Joint State-Parameter Estimation N. Santitissadeekorn & C. Jones https://doi.org/10.1175/MWR-D-14-00176.1
4. Coupled Bayesian inversion of time-lapse dispersive ground penetrating radar data to estimate soil hydraulic parameters Y. Yu et al. https://doi.org/10.1016/j.jhydrol.2026.135435
5. An approach to estimating and extrapolating model error based on inverse problem methods: towards accurate numerical weather prediction S. Hu et al. https://doi.org/10.1088/1674-1056/23/8/089201
6. Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors J. Tian et al. https://doi.org/10.1016/j.rse.2021.112802
7. Progress in integrating remote sensing data and hydrologic modeling X. Xu et al. https://doi.org/10.1177/0309133314536583
8. 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
9. Reducing Forecast Errors Due to Model Imperfections Using Ensemble Kalman Filtering H. Koyama & M. Watanabe https://doi.org/10.1175/2010MWR3067.1
10. Quantification of Representation Error in the Neutral Winds and Ion Drifts Using Data Assimilation J. Hu et al. https://doi.org/10.1029/2023SW003609
11. Identification of an optimal derivatives approximation by variational data assimilation E. Kazantsev https://doi.org/10.1016/j.jcp.2009.09.018
12. Localized adaptive inflation in ensemble data assimilation for a radiation belt model H. Godinez & J. Koller https://doi.org/10.1029/2012SW000767
13. A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters T. Dong et al. https://doi.org/10.1080/10618600.2022.2107531
14. Data assimilation for identification of cardiovascular network characteristics R. Lal et al. https://doi.org/10.1002/cnm.2824
15. A hybrid space approach for ensemble‐based 4‐D variational data assimilation A. Shao et al. https://doi.org/10.1029/2009JD012032
16. Estimation of Data Assimilation Error: A Shallow-Water Model Study A. Vlasenko et al. https://doi.org/10.1175/MWR-D-13-00205.1