State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency

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Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter (2007, methodology): “The observations were assumed unbiased with error covariance R = (0.022 m3m 3)2 I, assuming zero cross-correlation between the observation errors [De Lannoy et al., 2007].”
Data assimilation: making sense of Earth Observation (2014, methodology): “…al., 2012); (iv) need to improve representation of observational and model errors, and specify biases in the observational and model information (De Lannoy et al., 2007b; Crow and Reichle, 2008; Reichle et al., 2008; De Lannoy et al., 2009; Crow and van den Berg, 2010); and (v) need to have…”
The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter (2011, background): “The improvement of hydrologic model results through the assimilation of soil moisture data has been the subject of numerous studies (Entekhabi et al., 1994; Walker et al., 2002; Pauwels et al., 2002; De Lannoy et al., 2007a).”
State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter (2009, methodology): “These new techniques may improve the prediction accuracy of hydrologic modeling by assimilating sequential measurements [e.g., Weerts and El Serafy, 2006; De Lannoy et al., 2007].”