Nonlinearity in Data Assimilation Applications: A Practical Method for Analysis

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A Method for Assimilation of Lagrangian Data (2003, background): “…is connected with the difficulties, caused by an exponential separation rate in the vicinity of unstable critical points, which, in its Eulerian analog, is known as a cause of divergence of the EKF (Ide and Ghil 1998; Miller et al. 1994; Evensen 1997; Miller et al. 1999; Verlaan and Heemink 2001).”
Data assimilation for transient flow in geologic formations via ensemble Kalman filter (2006, background): “EnKF is suitable for handling nonlinearities and large dimensions and is easy to implement [25].”
Combining geostatistics and Kalman filtering for data assimilation in an estuarine system (2002, methodology): “Verlaan and Heemink [38] also applied the two techniques to the Lorenz equations and found that the EnKF was more reliable for highly nonlinear dynamics.”
Sequential Data Assimilation Techniques in Oceanography (2003, methodology): “Some of them have been compared experimentally to simulated test cases (Cañizares, 1999; Verlaan, 1998; Pham, 2000; Verlaan & Heemink, 1999) focusing on computer time, robustness of the algorithms to nonlinearities (Verlaan & Heemink, 1999), and robustness to systematic errors and to incorrect…”