Model Error Estimation Employing an Ensemble Data Assimilation Approach

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Using the ensemble Kalman filter to estimate multiplicative model parameters (2008, methodology): “In the state augmented EnKF, correlations between the model state and the model parameters are estimated from a finite ensemble (Baek et al., 2006; Zupanski and Zupanski, 2006).”
Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF) (2007, methodology): “…further investigated under various data assimilation methods, including Kalman filter, variational, geostatistical and ensemble-based methods [e.g., Bennett et al. , 1993; Dee , 1995; Reichle et al. , 2002; Nichols , 2003; Michalak et al. , 2004; Tsyrulnikov , 2005; Zupanski and Zupanski , 2006].”
Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters (2012, methodology): “The state augmentation method, which appends parameters to the model state vector is often used to estimate the unknown model parameters [Annan and Hargreaves, 2004; Zupanski and Zupanski, 2006; Kondrashov et al., 2008; Koyama and Watanabe, 2010].”