An iterative ensemble Kalman smoother

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Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “These and other pros and cons of both approaches have been discussed by Lorenc (2003); Kalnay, Li, Miyoshi, Yang, and Ballabrera-Poy (2007); Yang, Corazza, Carrassi, Kalnay, and Miyoshi (2009); and Bocquet and Sakov (2013).”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, methodology): “Ensemble methods for joint state and parameter estimation have recently been systematically developed [Bocquet and Sakov, 2013, 2014; Carrassi et al., 2017], and they are emerging as a promising way to solve inverse problems and to obtain qualitative estimates of uncertainty.”
Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, methodology): “With a two-dimensional forced turbulence model, driven by the barotropic vorticity advection equation, it was found to perform almost as well as the ETKF with optimally tuned uniform inflation (Bocquet and Sakov, 2014), although the local EnKF-N has not yet been thoroughly tested with this model.”